This notebook is from databricks document: - https://docs.databricks.com/_static/notebooks/package-cells.html
As you know, we need to eventually package and deploy our models, say using sbt.
However it is nice to be in a notebook environment to prototype and build intuition and create better pipelines.
Using package cells we can be in the best of both worlds to an extent.
Package Cells
Package cells are special cells that get compiled when executed. These cells have no visibility with respect to the rest of the notebook. You may think of them as separate scala files.
This means that only class and object definitions may go inside this cell. You may not have any variable or function definitions lying around by itself. The following cell will not work.
If you wish to use custom classes and/or objects defined within notebooks reliably in Spark, and across notebook sessions, you must use package cells to define those classes.
Unless you use package cells to define classes, you may also come across obscure bugs as follows:
// We define a class
case class TestKey(id: Long, str: String)
defined class TestKey
// we use that class as a key in the group by
val rdd = sc.parallelize(Array((TestKey(1L, "abd"), "dss"), (TestKey(2L, "ggs"), "dse"), (TestKey(1L, "abd"), "qrf")))
rdd.groupByKey().collect
rdd: org.apache.spark.rdd.RDD[(TestKey, String)] = ParallelCollectionRDD[0] at parallelize at command-2971213210276613:2
res0: Array[(TestKey, Iterable[String])] = Array((TestKey(1,abd),CompactBuffer(dss)), (TestKey(1,abd),CompactBuffer(qrf)), (TestKey(2,ggs),CompactBuffer(dse)))
What went wrong above? Even though we have two elements for the key TestKey(1L, "abd"), they behaved as two different keys resulting in:
Array[(TestKey, Iterable[String])] = Array(
(TestKey(2,ggs),CompactBuffer(dse)),
(TestKey(1,abd),CompactBuffer(dss)),
(TestKey(1,abd),CompactBuffer(qrf)))
Once we define our case class within a package cell, we will not face this issue.
package com.databricks.example
case class TestKey(id: Long, str: String)
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import com.databricks.example
val rdd = sc.parallelize(Array(
(example.TestKey(1L, "abd"), "dss"), (example.TestKey(2L, "ggs"), "dse"), (example.TestKey(1L, "abd"), "qrf")))
rdd.groupByKey().collect
import com.databricks.example
rdd: org.apache.spark.rdd.RDD[(com.databricks.example.TestKey, String)] = ParallelCollectionRDD[2] at parallelize at command-2971213210276616:3
res2: Array[(com.databricks.example.TestKey, Iterable[String])] = Array((TestKey(1,abd),CompactBuffer(dss, qrf)), (TestKey(2,ggs),CompactBuffer(dse)))
As you can see above, the group by worked above, grouping two elements (dss, qrf) under TestKey(1,abd).
These cells behave as individual source files, therefore only classes and objects can be defined inside these cells.
package x.y.z
val aNumber = 5 // won't work
def functionThatWillNotWork(a: Int): Int = a + 1
The following cell is the way to go.
package x.y.z
object Utils {
val aNumber = 5 // works!
def functionThatWillWork(a: Int): Int = a + 1
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.z.Utils
Utils.functionThatWillWork(Utils.aNumber)
import x.y.z.Utils
res5: Int = 6
Why did we get the warning: classes defined within packages cannot be redefined without a cluster restart?
Classes that get compiled with the package cells get dynamically injected into Spark's classloader. Currently it's not possible to remove classes from Spark's classloader. Any classes that you define and compile will have precedence in the classloader, therefore once you recompile, it will not be visible to your application.
Well that kind of beats the purpose of iterative notebook development if I have to restart the cluster, right? In that case, you may just rename the package to x.y.z2 during development/fast iteration and fix it once everything works.
One thing to remember with package cells is that it has no visiblity regarding the notebook environment.
- The SparkContext will not be defined as
sc. - The SQLContext will not be defined as
sqlContext. - Did you import a package in a separate cell? Those imports will not be available in the package cell and have to be remade.
- Variables imported through
%runcells will not be available.
It is really a standalone file that just looks like a cell in a notebook. This means that any function that uses anything that was defined in a separate cell, needs to take that variable as a parameter or the class needs to take it inside the constructor.
package x.y.zpackage
import org.apache.spark.SparkContext
case class IntArray(values: Array[Int])
class MyClass(sc: SparkContext) {
def sparkSum(array: IntArray): Int = {
sc.parallelize(array.values).reduce(_ + _)
}
}
object MyClass {
def sparkSum(sc: SparkContext, array: IntArray): Int = {
sc.parallelize(array.values).reduce(_ + _)
}
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.zpackage._
val array = IntArray(Array(1, 2, 3, 4, 5))
val myClass = new MyClass(sc)
myClass.sparkSum(array)
import x.y.zpackage._
array: x.y.zpackage.IntArray = IntArray([I@44a91426)
myClass: x.y.zpackage.MyClass = x.y.zpackage.MyClass@5d3b142
res7: Int = 15
MyClass.sparkSum(sc, array)
res8: Int = 15
Build Packages Locally
Although package cells are quite handy for quick prototyping in notebook environemnts, it is better to develop packages locally on your laptop and then upload the packaged or assembled jar file into databricks to use the classes and methods developed in the package.
Core ideas in Monte Carlo simulation
- modular arithmetic gives pseudo-random streams that are indistiguishable from 'true' Uniformly distributed samples in integers from \({0,1,2,...,m}\)
- by diving the integer streams from above by \(m\) we get samples from \({0/m,1/m,...,(m-1)/m}\) and "pretend" this to be samples from the Uniform(0,1) RV
- we can use inverse distribution function of von Neumann's rejection sampler to convert samples from Uniform(0,1) RV to the following:
- any other random variable
- vector of random variables that could be dependent
- or more generally other random structures:
- random graphs and networks
- random walks or (sensible perturbations of live traffic data on open street maps for hypothesis tests)
- models of interacting paticle systems in ecology / chemcal physics, etc...
- https://en.wikipedia.org/wiki/Inversetransformsampling
- https://en.wikipedia.org/wiki/Rejection_sampling - will revisit below for Expoential RV
breeze.stats.distributions
Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.
import breeze.stats.distributions._
val poi = new Poisson(3.0);
import breeze.stats.distributions._
poi: breeze.stats.distributions.Poisson = Poisson(3.0)
val s = poi.sample(5); // let's draw five samples - black-box
s: IndexedSeq[Int] = Vector(2, 4, 3, 4, 6)
Getting probabilities of the Poisson samples
s.map( x => poi.probabilityOf(x) ) // PMF
res0: IndexedSeq[Double] = Vector(0.22404180765538775, 0.16803135574154085, 0.22404180765538775, 0.16803135574154085, 0.05040940672246224)
val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = MappedRand(Poisson(3.0),$Lambda$5909/1062631843@70cdbd4c)
breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
res1: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(3.0660000000000034,3.250894894894892,1000)
(poi.mean, poi.variance) // population mean and variance
res2: (Double, Double) = (3.0,3.0)
Exponential random Variable
Let's focus on getting our hands direty with a common random variable.
NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.
val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)
expo.rate // what is the rate parameter
res3: Double = 0.5
A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.
expo.probability(0, math.log(2) * expo.rate)
res4: Double = 0.5
expo.probability(math.log(2) * expo.rate, 10000.0)
res5: Double = 0.5
expo.probability(0.0, 1.5)
res6: Double = 0.950212931632136
The above result means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well.
1 - math.exp(-3.0) // the CDF of the Exponential RV with rate parameter 3
res7: Double = 0.950212931632136
val samples = expo.sample(2).sorted; // built-in black box - we will roll our own shortly in Spark
samples: IndexedSeq[Double] = Vector(2.412493444974671, 3.6802759985818687)
expo.probability(samples(0), samples(1));
res8: Double = 0.007390811722912227
breeze.stats.meanAndVariance(expo.samples.take(10000)); // mean and variance of the sample
res9: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(2.0109026469746554,4.073504140726905,10000)
(1 / expo.rate, 1 / (expo.rate * expo.rate)) // mean and variance of the population
res10: (Double, Double) = (2.0,4.0)
import spark.implicits._
import org.apache.spark.sql.functions._
import spark.implicits._
import org.apache.spark.sql.functions._
val df = spark.range(1000).toDF("Id") // just make a DF of 1000 row indices
df: org.apache.spark.sql.DataFrame = [Id: bigint]
df.show(5)
+---+
| Id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id| rand|
+---+--------------------+
| 0|0.024042816995877625|
| 1| 0.4763832311243641|
| 2| 0.3392911406256911|
| 3| 0.6282132043405342|
| 4| 0.9665960987271114|
+---+--------------------+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id| rand|
+---+--------------------+
| 0|0.024042816995877625|
| 1| 0.4763832311243641|
| 2| 0.3392911406256911|
| 3| 0.6282132043405342|
| 4| 0.9665960987271114|
+---+--------------------+
only showing top 5 rows
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
val dfRand = df.select($"Id", rand(seed=879664) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+-------------------+
| Id| rand|
+---+-------------------+
| 0| 0.269224348263137|
| 1|0.20433965628039852|
| 2| 0.8481228617934538|
| 3| 0.6665103087537884|
| 4| 0.7712898780552342|
+---+-------------------+
only showing top 5 rows
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
Let's use the inverse CDF of the Exponential RV to transform these samples from the Uniform(0,1) RV into those from the Exponential RV.
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
.withColumn("one",lit(1.0))
.withColumn("rate",lit(0.5))
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 2 more fields]
dfRand.show(5)
+---+--------------------+---+----+
| Id| rand|one|rate|
+---+--------------------+---+----+
| 0|0.024042816995877625|1.0| 0.5|
| 1| 0.4763832311243641|1.0| 0.5|
| 2| 0.3392911406256911|1.0| 0.5|
| 3| 0.6282132043405342|1.0| 0.5|
| 4| 0.9665960987271114|1.0| 0.5|
+---+--------------------+---+----+
only showing top 5 rows
val dfExpRand = dfRand.withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand")) // samples from expo(rate=0.5)
dfExpRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
dfExpRand.show(5)
+---+--------------------+---+----+--------------------+
| Id| rand|one|rate| expo_sample|
+---+--------------------+---+----+--------------------+
| 0|0.024042816995877625|1.0| 0.5|0.048673126808362034|
| 1| 0.4763832311243641|1.0| 0.5| 1.2939904386814756|
| 2| 0.3392911406256911|1.0| 0.5| 0.8288839819270684|
| 3| 0.6282132043405342|1.0| 0.5| 1.9788694379168241|
| 4| 0.9665960987271114|1.0| 0.5| 6.798165162486205|
+---+--------------------+---+----+--------------------+
only showing top 5 rows
display(dfExpRand)
| Id | rand | one | rate | expo_sample |
|---|---|---|---|---|
| 0.0 | 2.4042816995877625e-2 | 1.0 | 0.5 | 4.8673126808362034e-2 |
| 1.0 | 0.4763832311243641 | 1.0 | 0.5 | 1.2939904386814756 |
| 2.0 | 0.3392911406256911 | 1.0 | 0.5 | 0.8288839819270684 |
| 3.0 | 0.6282132043405342 | 1.0 | 0.5 | 1.9788694379168241 |
| 4.0 | 0.9665960987271114 | 1.0 | 0.5 | 6.798165162486205 |
| 5.0 | 0.8269758822163711 | 1.0 | 0.5 | 3.508648570093974 |
| 6.0 | 0.3404052214826948 | 1.0 | 0.5 | 0.8322592089262 |
| 7.0 | 0.5658253891225227 | 1.0 | 0.5 | 1.6686169931673005 |
| 8.0 | 0.11737981749647353 | 1.0 | 0.5 | 0.24972063061386013 |
| 9.0 | 0.7848668745585088 | 1.0 | 0.5 | 3.072996508744745 |
| 10.0 | 0.10665116151243736 | 1.0 | 0.5 | 0.2255562755600773 |
| 11.0 | 0.34971775733163646 | 1.0 | 0.5 | 0.8606975816973311 |
| 12.0 | 3.349566781262969e-2 | 1.0 | 0.5 | 6.813899599567436e-2 |
| 13.0 | 0.334063669089834 | 1.0 | 0.5 | 0.8131224244912618 |
| 14.0 | 0.4105052702588069 | 1.0 | 0.5 | 1.0569789985263547 |
| 15.0 | 0.22519777483698333 | 1.0 | 0.5 | 0.5102949510683874 |
| 16.0 | 0.21124107883891907 | 1.0 | 0.5 | 0.47458910937069004 |
| 17.0 | 0.12332274767843698 | 1.0 | 0.5 | 0.26323273531964136 |
| 18.0 | 4.324422155449681e-2 | 1.0 | 0.5 | 8.841423006745963e-2 |
| 19.0 | 3.933267172708754e-2 | 1.0 | 0.5 | 8.025420479220831e-2 |
| 20.0 | 0.8162723223794007 | 1.0 | 0.5 | 3.388601261211285 |
| 21.0 | 7.785566240785313e-2 | 1.0 | 0.5 | 0.16210703862675058 |
| 22.0 | 0.1244015150504949 | 1.0 | 0.5 | 0.26569528726942954 |
| 23.0 | 0.5313795676534989 | 1.0 | 0.5 | 1.5159243018004147 |
| 24.0 | 0.8177063965639969 | 1.0 | 0.5 | 3.4042733720631824 |
| 25.0 | 0.7757379079578416 | 1.0 | 0.5 | 2.98987971469377 |
| 26.0 | 0.9019086568714294 | 1.0 | 0.5 | 4.643712323362237 |
| 27.0 | 0.5793202308042869 | 1.0 | 0.5 | 1.7317667559523853 |
| 28.0 | 4.913530139386124e-2 | 1.0 | 0.5 | 0.10076699863506142 |
| 29.0 | 0.6984668419742928 | 1.0 | 0.5 | 2.3977505839877837 |
| 30.0 | 1.579141234703818e-2 | 1.0 | 0.5 | 3.18348501444197e-2 |
| 31.0 | 0.3170147319677016 | 1.0 | 0.5 | 0.762563978285606 |
| 32.0 | 0.6243818511105477 | 1.0 | 0.5 | 1.9583644261768265 |
| 33.0 | 0.9720896733059052 | 1.0 | 0.5 | 7.157517052464175 |
| 34.0 | 6.406755887221727e-2 | 1.0 | 0.5 | 0.13242396678361196 |
| 35.0 | 0.8275324400058236 | 1.0 | 0.5 | 3.515092236401242 |
| 36.0 | 0.4223760255739688 | 1.0 | 0.5 | 1.0976643705892708 |
| 37.0 | 0.6237792697270514 | 1.0 | 0.5 | 1.9551585184791915 |
| 38.0 | 0.30676209726152626 | 1.0 | 0.5 | 0.7327640894184466 |
| 39.0 | 0.7209798363387625 | 1.0 | 0.5 | 2.5529424571699533 |
| 40.0 | 0.5408086036735253 | 1.0 | 0.5 | 1.556576340750279 |
| 41.0 | 0.22254408000797787 | 1.0 | 0.5 | 0.5034566621599345 |
| 42.0 | 0.6478051949133747 | 1.0 | 0.5 | 2.0871416658496442 |
| 43.0 | 0.6186563924196967 | 1.0 | 0.5 | 1.9281089062362353 |
| 44.0 | 0.2689179760895458 | 1.0 | 0.5 | 0.6264592354306907 |
| 45.0 | 0.1310898372764776 | 1.0 | 0.5 | 0.28103107825164747 |
| 46.0 | 0.8459785027719904 | 1.0 | 0.5 | 3.741326187841892 |
| 47.0 | 0.7844461581951896 | 1.0 | 0.5 | 3.069089109365284 |
| 48.0 | 0.3195541476806115 | 1.0 | 0.5 | 0.7700140609818293 |
| 49.0 | 0.326892158146161 | 1.0 | 0.5 | 0.7916994433570004 |
| 50.0 | 0.4392312327086285 | 1.0 | 0.5 | 1.1568932758900772 |
| 51.0 | 0.5377790694946909 | 1.0 | 0.5 | 1.543424595293625 |
| 52.0 | 0.4369640582174875 | 1.0 | 0.5 | 1.1488236262486446 |
| 53.0 | 0.26672242876690055 | 1.0 | 0.5 | 0.6204619408451196 |
| 54.0 | 0.9743181848790359 | 1.0 | 0.5 | 7.323944240755433 |
| 55.0 | 0.5636877255447679 | 1.0 | 0.5 | 1.6587941323713102 |
| 56.0 | 0.8214194684040824 | 1.0 | 0.5 | 3.44543124420649 |
| 57.0 | 4.2394218638494574e-2 | 1.0 | 0.5 | 8.663817482333575e-2 |
| 58.0 | 0.2986030735782996 | 1.0 | 0.5 | 0.7093626466953578 |
| 59.0 | 0.9251464009715654 | 1.0 | 0.5 | 5.184442172120483 |
| 60.0 | 0.9768531076059933 | 1.0 | 0.5 | 7.531789490600966 |
| 61.0 | 0.4147651110232632 | 1.0 | 0.5 | 1.0714839854387161 |
| 62.0 | 0.4525161010109643 | 1.0 | 0.5 | 1.2048444519262322 |
| 63.0 | 7.638776464132979e-2 | 1.0 | 0.5 | 0.1589259082490955 |
| 64.0 | 0.8203900440907219 | 1.0 | 0.5 | 3.433935381714252 |
| 65.0 | 0.9746746584977457 | 1.0 | 0.5 | 7.35189948830076 |
| 66.0 | 0.41832172801299305 | 1.0 | 0.5 | 1.083675562745256 |
| 67.0 | 3.3131200470495226e-2 | 1.0 | 0.5 | 6.738494114620364e-2 |
| 68.0 | 0.10008247055930286 | 1.0 | 0.5 | 0.21090430762250917 |
| 69.0 | 0.4268021831375646 | 1.0 | 0.5 | 1.113048783438383 |
| 70.0 | 0.8213380254753332 | 1.0 | 0.5 | 3.444519337826204 |
| 71.0 | 0.47431121070064797 | 1.0 | 0.5 | 1.2860917933318652 |
| 72.0 | 0.68679164992865 | 1.0 | 0.5 | 2.321773309424188 |
| 73.0 | 0.7057803676609208 | 1.0 | 0.5 | 2.4468574835462356 |
| 74.0 | 0.32184620342731496 | 1.0 | 0.5 | 0.776762356325894 |
| 75.0 | 0.3589329148022541 | 1.0 | 0.5 | 0.8892423408857425 |
| 76.0 | 0.7448782338623747 | 1.0 | 0.5 | 2.7320286670643386 |
| 77.0 | 0.9740613004640858 | 1.0 | 0.5 | 7.304038469785621 |
| 78.0 | 0.7453909609063684 | 1.0 | 0.5 | 2.736052180743762 |
| 79.0 | 6.853610233988816e-2 | 1.0 | 0.5 | 0.14199569380051374 |
| 80.0 | 0.336012781812836 | 1.0 | 0.5 | 0.8189847588182159 |
| 81.0 | 0.2963058480260412 | 1.0 | 0.5 | 0.7028229208813994 |
| 82.0 | 0.38627232525200883 | 1.0 | 0.5 | 0.9764079513820425 |
| 83.0 | 0.9097248909817963 | 1.0 | 0.5 | 4.809787008391862 |
| 84.0 | 0.20679745942261907 | 1.0 | 0.5 | 0.46335335878970363 |
| 85.0 | 0.16582497569469312 | 1.0 | 0.5 | 0.36262407472768277 |
| 86.0 | 0.6960738838737711 | 1.0 | 0.5 | 2.381941292346302 |
| 87.0 | 0.890094879396298 | 1.0 | 0.5 | 4.416275650715409 |
| 88.0 | 0.8258738496535992 | 1.0 | 0.5 | 3.4959504809275685 |
| 89.0 | 0.4066244237870885 | 1.0 | 0.5 | 1.0438554620679272 |
| 90.0 | 0.44414816275414526 | 1.0 | 0.5 | 1.1745070000344822 |
| 91.0 | 0.8820223175378497 | 1.0 | 0.5 | 4.274519608161631 |
| 92.0 | 0.3814200182827251 | 1.0 | 0.5 | 0.9606575597598919 |
| 93.0 | 0.491252177347836 | 1.0 | 0.5 | 1.3516056440673538 |
| 94.0 | 0.9042145561359308 | 1.0 | 0.5 | 4.691289097027222 |
| 95.0 | 0.8293982814391448 | 1.0 | 0.5 | 3.536847140695551 |
| 96.0 | 0.6957817964411346 | 1.0 | 0.5 | 2.38002012037681 |
| 97.0 | 0.46108317902841456 | 1.0 | 0.5 | 1.2363880819986401 |
| 98.0 | 0.658234439306857 | 1.0 | 0.5 | 2.14726054405596 |
| 99.0 | 0.43175037735005384 | 1.0 | 0.5 | 1.130388960612226 |
| 100.0 | 0.7719881477151886 | 1.0 | 0.5 | 2.9567153353469786 |
| 101.0 | 6.397418621476536e-2 | 1.0 | 0.5 | 0.13222444810891013 |
| 102.0 | 0.44410081132020296 | 1.0 | 0.5 | 1.1743366329905425 |
| 103.0 | 0.9364231799229901 | 1.0 | 0.5 | 5.511012678534471 |
| 104.0 | 0.5453471566845608 | 1.0 | 0.5 | 1.576442265948382 |
| 105.0 | 0.7346238968987211 | 1.0 | 0.5 | 2.653214404406468 |
| 106.0 | 0.3493365535175731 | 1.0 | 0.5 | 0.8595254994862054 |
| 107.0 | 0.5230037795263933 | 1.0 | 0.5 | 1.480493423321206 |
| 108.0 | 0.6886814284788837 | 1.0 | 0.5 | 2.333877090719847 |
| 109.0 | 0.7731252946239237 | 1.0 | 0.5 | 2.966714745163421 |
| 110.0 | 2.276168830750991e-2 | 1.0 | 0.5 | 4.604946957472576e-2 |
| 111.0 | 0.264951110753621 | 1.0 | 0.5 | 0.6156365319998442 |
| 112.0 | 0.14616140102686104 | 1.0 | 0.5 | 0.3160261944637388 |
| 113.0 | 3.066619523108849e-2 | 1.0 | 0.5 | 6.229248529326732e-2 |
| 114.0 | 0.49017052188807597 | 1.0 | 0.5 | 1.3473579316333943 |
| 115.0 | 9.835381148010536e-2 | 1.0 | 0.5 | 0.20706617613153588 |
| 116.0 | 8.828156810764243e-2 | 1.0 | 0.5 | 0.1848481470740138 |
| 117.0 | 0.6868446103815881 | 1.0 | 0.5 | 2.3221115183574854 |
| 118.0 | 0.4067862578001439 | 1.0 | 0.5 | 1.0444010055396156 |
| 119.0 | 0.44070042328022496 | 1.0 | 0.5 | 1.1621400679168603 |
| 120.0 | 0.19523663294276805 | 1.0 | 0.5 | 0.4344139974853618 |
| 121.0 | 0.34930320794183467 | 1.0 | 0.5 | 0.8594230049585315 |
| 122.0 | 0.37713232784782635 | 1.0 | 0.5 | 0.9468423740115679 |
| 123.0 | 0.6429635193876022 | 1.0 | 0.5 | 2.0598346316676848 |
| 124.0 | 0.5726782020473655 | 1.0 | 0.5 | 1.7004358488091253 |
| 125.0 | 0.3013872989669776 | 1.0 | 0.5 | 0.7173175321607891 |
| 126.0 | 0.3469657736553403 | 1.0 | 0.5 | 0.8522514741503977 |
| 127.0 | 0.3062597218624077 | 1.0 | 0.5 | 0.7313152550300903 |
| 128.0 | 0.875187814442521 | 1.0 | 0.5 | 4.161890374256847 |
| 129.0 | 0.4331447487608374 | 1.0 | 0.5 | 1.1353025933318837 |
| 130.0 | 0.40669672646934574 | 1.0 | 0.5 | 1.0440991764725864 |
| 131.0 | 0.10105613827873039 | 1.0 | 0.5 | 0.21306938341833667 |
| 132.0 | 0.7598058191143243 | 1.0 | 0.5 | 2.852615191501923 |
| 133.0 | 0.7882552857444408 | 1.0 | 0.5 | 3.104747815909758 |
| 134.0 | 0.41196255767852374 | 1.0 | 0.5 | 1.0619293113867083 |
| 135.0 | 0.23956793737837967 | 1.0 | 0.5 | 0.5477370075782519 |
| 136.0 | 0.301117813053034 | 1.0 | 0.5 | 0.716546192187808 |
| 137.0 | 0.4359866364410945 | 1.0 | 0.5 | 1.1453546670228079 |
| 138.0 | 0.9682651399507317 | 1.0 | 0.5 | 6.90067903242789 |
| 139.0 | 0.10678335714273168 | 1.0 | 0.5 | 0.22585225268919112 |
| 140.0 | 0.2693226978676214 | 1.0 | 0.5 | 0.6275667277003328 |
| 141.0 | 0.5668302775070247 | 1.0 | 0.5 | 1.6732513179468083 |
| 142.0 | 0.7096151948853401 | 1.0 | 0.5 | 2.473096642771549 |
| 143.0 | 0.7574375459472653 | 1.0 | 0.5 | 2.8329921185549365 |
| 144.0 | 0.49769652658104957 | 1.0 | 0.5 | 1.3771016264425564 |
| 145.0 | 0.45884572648841415 | 1.0 | 0.5 | 1.2281017543594028 |
| 146.0 | 0.3288386747446713 | 1.0 | 0.5 | 0.7974914915764556 |
| 147.0 | 0.5410136911709724 | 1.0 | 0.5 | 1.557469795251325 |
| 148.0 | 0.6336220553800798 | 1.0 | 0.5 | 2.008179685617141 |
| 149.0 | 0.375647544465191 | 1.0 | 0.5 | 0.9420804749655175 |
| 150.0 | 0.10142418148506294 | 1.0 | 0.5 | 0.21388838577034947 |
| 151.0 | 0.4721728293756773 | 1.0 | 0.5 | 1.2779727544450452 |
| 152.0 | 0.7518331384524283 | 1.0 | 0.5 | 2.787307860488268 |
| 153.0 | 6.676079335730689e-2 | 1.0 | 0.5 | 0.13818745319668635 |
| 154.0 | 0.8053497028664404 | 1.0 | 0.5 | 3.2731013568502365 |
| 155.0 | 0.6436250826649516 | 1.0 | 0.5 | 2.063543927419815 |
| 156.0 | 0.28432165233043427 | 1.0 | 0.5 | 0.6690488961123614 |
| 157.0 | 0.33131329522029473 | 1.0 | 0.5 | 0.8048792646192418 |
| 158.0 | 0.4390246975576103 | 1.0 | 0.5 | 1.1561567971907922 |
| 159.0 | 0.9576167619712271 | 1.0 | 0.5 | 6.322004648835221 |
| 160.0 | 0.2191897706358752 | 1.0 | 0.5 | 0.4948462856734829 |
| 161.0 | 1.1385964425825512e-2 | 1.0 | 0.5 | 2.2902561570486413e-2 |
| 162.0 | 0.679111841181116 | 1.0 | 0.5 | 2.2733252629142697 |
| 163.0 | 0.3189329784940914 | 1.0 | 0.5 | 0.768189122733912 |
| 164.0 | 0.5494024842158357 | 1.0 | 0.5 | 1.5943615282559738 |
| 165.0 | 0.4995339525325778 | 1.0 | 0.5 | 1.3844310395116766 |
| 166.0 | 0.6133797107105055 | 1.0 | 0.5 | 1.900624464472158 |
| 167.0 | 0.18020899363866738 | 1.0 | 0.5 | 0.3974116829996968 |
| 168.0 | 0.3549472573452819 | 1.0 | 0.5 | 0.8768463879435472 |
| 169.0 | 0.6992620601216817 | 1.0 | 0.5 | 2.403032050173198 |
| 170.0 | 0.24085612119427668 | 1.0 | 0.5 | 0.5511279118150403 |
| 171.0 | 0.1609790935708849 | 1.0 | 0.5 | 0.3510393091093769 |
| 172.0 | 0.9711366383834527 | 1.0 | 0.5 | 7.090364504579701 |
| 173.0 | 0.38455734034855193 | 1.0 | 0.5 | 0.9708269965922631 |
| 174.0 | 0.7140149247000265 | 1.0 | 0.5 | 2.503631307579515 |
| 175.0 | 0.6231707003505403 | 1.0 | 0.5 | 1.9519259602967969 |
| 176.0 | 0.6056033420465197 | 1.0 | 0.5 | 1.8607962600766825 |
| 177.0 | 0.9191984318132235 | 1.0 | 0.5 | 5.03151781078248 |
| 178.0 | 0.31549310135748787 | 1.0 | 0.5 | 0.7581131118739614 |
| 179.0 | 0.4450415722231543 | 1.0 | 0.5 | 1.1777241458955992 |
| 180.0 | 0.4583485174404076 | 1.0 | 0.5 | 1.2262650109539877 |
| 181.0 | 0.8948048795370758 | 1.0 | 0.5 | 4.503876726373103 |
| 182.0 | 0.5068846132533013 | 1.0 | 0.5 | 1.4140241642575553 |
| 183.0 | 0.474099338611806 | 1.0 | 0.5 | 1.2852858815130643 |
| 184.0 | 3.968996187382612e-2 | 1.0 | 0.5 | 8.099818055602134e-2 |
| 185.0 | 0.3812923011104965 | 1.0 | 0.5 | 0.9602446657347301 |
| 186.0 | 0.4490455490374048 | 1.0 | 0.5 | 1.1922062787516934 |
| 187.0 | 0.18885603597795153 | 1.0 | 0.5 | 0.4186194528265905 |
| 188.0 | 0.14835262533228888 | 1.0 | 0.5 | 0.32116543464514347 |
| 189.0 | 1.0239721160210324e-2 | 1.0 | 0.5 | 2.0585015521643695e-2 |
| 190.0 | 0.7812863611722196 | 1.0 | 0.5 | 3.0399839801249593 |
| 191.0 | 0.40315501297127665 | 1.0 | 0.5 | 1.032195705046974 |
| 192.0 | 0.27623054102229216 | 1.0 | 0.5 | 0.6465647282627558 |
| 193.0 | 0.3476188563715197 | 1.0 | 0.5 | 0.8542526234724834 |
| 194.0 | 0.40005917714351136 | 1.0 | 0.5 | 1.0218485144052543 |
| 195.0 | 0.690531564629077 | 1.0 | 0.5 | 2.3457983558717395 |
| 196.0 | 0.6243976540207664 | 1.0 | 0.5 | 1.9584485714328679 |
| 197.0 | 0.6936108435709636 | 1.0 | 0.5 | 2.365798463973219 |
| 198.0 | 0.5580754032112615 | 1.0 | 0.5 | 1.6332320139161962 |
| 199.0 | 0.5077201201493545 | 1.0 | 0.5 | 1.4174157254902722 |
| 200.0 | 0.24217287516689856 | 1.0 | 0.5 | 0.5545999737072358 |
| 201.0 | 0.5866156019146013 | 1.0 | 0.5 | 1.7667547458541368 |
| 202.0 | 0.13480908234777678 | 1.0 | 0.5 | 0.289610164710414 |
| 203.0 | 0.8592340571612042 | 1.0 | 0.5 | 3.921313495527709 |
| 204.0 | 0.5015919278028333 | 1.0 | 0.5 | 1.3926722308356132 |
| 205.0 | 0.9524596166650006 | 1.0 | 0.5 | 6.092351507324665 |
| 206.0 | 0.7897861372493181 | 1.0 | 0.5 | 3.1192597448504493 |
| 207.0 | 0.992502482003315 | 1.0 | 0.5 | 9.786366493971752 |
| 208.0 | 0.7190312712917515 | 1.0 | 0.5 | 2.539023803153757 |
| 209.0 | 4.905914294075553e-2 | 1.0 | 0.5 | 0.10060681726863402 |
| 210.0 | 7.292405751548614e-2 | 1.0 | 0.5 | 0.15143958783801956 |
| 211.0 | 0.7448249635342986 | 1.0 | 0.5 | 2.731611103575813 |
| 212.0 | 7.024810923770186e-2 | 1.0 | 0.5 | 0.14567502510922456 |
| 213.0 | 0.6499895525785409 | 1.0 | 0.5 | 2.0995845503371515 |
| 214.0 | 0.2347304081263114 | 1.0 | 0.5 | 0.5350541991171558 |
| 215.0 | 0.5554391604460018 | 1.0 | 0.5 | 1.62133672301354 |
| 216.0 | 0.45655849903065526 | 1.0 | 0.5 | 1.2196664242497346 |
| 217.0 | 0.7734426024060143 | 1.0 | 0.5 | 2.969513910302441 |
| 218.0 | 0.13396935067038884 | 1.0 | 0.5 | 0.28766995842079884 |
| 219.0 | 0.44732092861928796 | 1.0 | 0.5 | 1.1859555740131458 |
| 220.0 | 0.532360601482588 | 1.0 | 0.5 | 1.5201155921058849 |
| 221.0 | 0.8353842261129955 | 1.0 | 0.5 | 3.6082823273928657 |
| 222.0 | 0.4415319449291001 | 1.0 | 0.5 | 1.1651157196663489 |
| 223.0 | 7.107670903097285e-2 | 1.0 | 0.5 | 0.14745823038641734 |
| 224.0 | 0.2587810776672368 | 1.0 | 0.5 | 0.5989185111514972 |
| 225.0 | 0.8783986898097359 | 1.0 | 0.5 | 4.214015069834256 |
| 226.0 | 0.8772054215086009 | 1.0 | 0.5 | 4.194484826673072 |
| 227.0 | 0.8792794678306639 | 1.0 | 0.5 | 4.228554112475122 |
| 228.0 | 0.9704733519907857 | 1.0 | 0.5 | 7.04492420208042 |
| 229.0 | 0.5911949898493493 | 1.0 | 0.5 | 1.7890339688352386 |
| 230.0 | 0.1881415659970821 | 1.0 | 0.5 | 0.4168585927615873 |
| 231.0 | 0.27280409460696464 | 1.0 | 0.5 | 0.6371187335647226 |
| 232.0 | 0.8997405595900834 | 1.0 | 0.5 | 4.599988097103155 |
| 233.0 | 0.805703299381296 | 1.0 | 0.5 | 3.2767378072078768 |
| 234.0 | 0.12469160466325713 | 1.0 | 0.5 | 0.26635800581530467 |
| 235.0 | 4.5007500538482015e-2 | 1.0 | 0.5 | 9.210358499849247e-2 |
| 236.0 | 0.5406049480217902 | 1.0 | 0.5 | 1.5556895188057671 |
| 237.0 | 0.7089850749112344 | 1.0 | 0.5 | 2.4687614483220965 |
| 238.0 | 0.4050350886413143 | 1.0 | 0.5 | 1.038505695363729 |
| 239.0 | 0.18681323647475456 | 1.0 | 0.5 | 0.4135889487659769 |
| 240.0 | 0.23557258393827696 | 1.0 | 0.5 | 0.5372564022794455 |
| 241.0 | 0.32077303736600393 | 1.0 | 0.5 | 0.7735998942749642 |
| 242.0 | 0.27136665743377386 | 1.0 | 0.5 | 0.6331692658855377 |
| 243.0 | 8.600287997937284e-2 | 1.0 | 0.5 | 0.1798557169901321 |
| 244.0 | 0.7600332087019175 | 1.0 | 0.5 | 2.8545094696108473 |
| 245.0 | 0.41783858737032487 | 1.0 | 0.5 | 1.0820150568291664 |
| 246.0 | 0.8293413803867148 | 1.0 | 0.5 | 3.5361801888515507 |
| 247.0 | 0.7333405374926266 | 1.0 | 0.5 | 2.6435657118891944 |
| 248.0 | 0.8772160342203914 | 1.0 | 0.5 | 4.194657687243259 |
| 249.0 | 5.301719603632349e-2 | 1.0 | 0.5 | 0.10894868878842578 |
| 250.0 | 0.9026197915984311 | 1.0 | 0.5 | 4.658264576287863 |
| 251.0 | 0.23914978744357662 | 1.0 | 0.5 | 0.5466375404979962 |
| 252.0 | 0.4968410398019236 | 1.0 | 0.5 | 1.3736982691137887 |
| 253.0 | 0.5384099353933083 | 1.0 | 0.5 | 1.5461561756710942 |
| 254.0 | 0.8907377100572391 | 1.0 | 0.5 | 4.428007915156274 |
| 255.0 | 0.9066080833593035 | 1.0 | 0.5 | 4.741900966190897 |
| 256.0 | 0.6829340520390256 | 1.0 | 0.5 | 2.297290978019848 |
| 257.0 | 0.6571961620790489 | 1.0 | 0.5 | 2.1411937930382208 |
| 258.0 | 0.3669337202760652 | 1.0 | 0.5 | 0.9143603100291875 |
| 259.0 | 0.5097789017273943 | 1.0 | 0.5 | 1.4257975373650602 |
| 260.0 | 0.19359708351354943 | 1.0 | 0.5 | 0.4303435299938143 |
| 261.0 | 0.4198903553742862 | 1.0 | 0.5 | 1.0890763016996372 |
| 262.0 | 0.8275841161941834 | 1.0 | 0.5 | 3.515691583104315 |
| 263.0 | 0.5245014108343904 | 1.0 | 0.5 | 1.486782728111085 |
| 264.0 | 0.20212413144380958 | 1.0 | 0.5 | 0.45160449363942295 |
| 265.0 | 0.6588746438800428 | 1.0 | 0.5 | 2.1510105119936225 |
| 266.0 | 0.6090021282726809 | 1.0 | 0.5 | 1.8781063243284435 |
| 267.0 | 0.8237249377890233 | 1.0 | 0.5 | 3.4714193009141487 |
| 268.0 | 0.39616901908028135 | 1.0 | 0.5 | 1.0089219062444619 |
| 269.0 | 0.46974976524528655 | 1.0 | 0.5 | 1.268812485625708 |
| 270.0 | 0.1430991547459871 | 1.0 | 0.5 | 0.30886613379677225 |
| 271.0 | 0.8900411063855386 | 1.0 | 0.5 | 4.415297354889731 |
| 272.0 | 0.8908538606807918 | 1.0 | 0.5 | 4.430135134053363 |
| 273.0 | 0.7571966906626759 | 1.0 | 0.5 | 2.8310071799990957 |
| 274.0 | 0.45373865486682496 | 1.0 | 0.5 | 1.2093155273309903 |
| 275.0 | 0.5105750301805768 | 1.0 | 0.5 | 1.429048215969818 |
| 276.0 | 0.8703161512938904 | 1.0 | 0.5 | 4.085311447017532 |
| 277.0 | 0.6263910497078812 | 1.0 | 0.5 | 1.9690912320594496 |
| 278.0 | 0.8879229935650624 | 1.0 | 0.5 | 4.377138172983162 |
| 279.0 | 0.4592765782031528 | 1.0 | 0.5 | 1.2296947319737286 |
| 280.0 | 0.3332564208219061 | 1.0 | 0.5 | 0.8106994919909759 |
| 281.0 | 0.7769330816977306 | 1.0 | 0.5 | 3.000566940929362 |
| 282.0 | 0.47688504943059673 | 1.0 | 0.5 | 1.2959080966079761 |
| 283.0 | 0.5519970113210297 | 1.0 | 0.5 | 1.6059107508619763 |
| 284.0 | 0.6116679748262741 | 1.0 | 0.5 | 1.8917891406149387 |
| 285.0 | 0.23344992857646873 | 1.0 | 0.5 | 0.5317105161007432 |
| 286.0 | 0.547367711693101 | 1.0 | 0.5 | 1.5853504174370916 |
| 287.0 | 0.45229676089796134 | 1.0 | 0.5 | 1.2040433464044202 |
| 288.0 | 0.2232542399926749 | 1.0 | 0.5 | 0.5052843787124497 |
| 289.0 | 4.812719615823391e-2 | 1.0 | 0.5 | 9.864772505442727e-2 |
| 290.0 | 0.9924754840764293 | 1.0 | 0.5 | 9.779177599019986 |
| 291.0 | 0.23112957413067814 | 1.0 | 0.5 | 0.525665641185302 |
| 292.0 | 0.7136348699646448 | 1.0 | 0.5 | 2.500975207955907 |
| 293.0 | 0.7097521146725535 | 1.0 | 0.5 | 2.474039888248458 |
| 294.0 | 0.2534528100999812 | 1.0 | 0.5 | 0.584592898262904 |
| 295.0 | 0.14159162922836588 | 1.0 | 0.5 | 0.30535067223059725 |
| 296.0 | 0.286451779155965 | 1.0 | 0.5 | 0.6750105216507689 |
| 297.0 | 0.9194163349443866 | 1.0 | 0.5 | 5.0369186336261 |
| 298.0 | 0.8565261729771216 | 1.0 | 0.5 | 3.8832053010040535 |
| 299.0 | 0.9963446427954837 | 1.0 | 0.5 | 11.223122920363782 |
| 300.0 | 0.44312606197302895 | 1.0 | 0.5 | 1.1708327755623118 |
| 301.0 | 0.3333780003343948 | 1.0 | 0.5 | 0.8110642217087809 |
| 302.0 | 8.100442373331274e-2 | 1.0 | 0.5 | 0.16894794055205103 |
| 303.0 | 0.9114279295358697 | 1.0 | 0.5 | 4.847877405697818 |
| 304.0 | 0.1420851500734669 | 1.0 | 0.5 | 0.30650085385781334 |
| 305.0 | 0.3998938086649432 | 1.0 | 0.5 | 1.0212973077353178 |
| 306.0 | 0.43509561875462044 | 1.0 | 0.5 | 1.1421975977833791 |
| 307.0 | 0.2607501670030833 | 1.0 | 0.5 | 0.6042386923167598 |
| 308.0 | 0.7861629300585208 | 1.0 | 0.5 | 3.0850818187043214 |
| 309.0 | 0.4238639225351366 | 1.0 | 0.5 | 1.1028228011782435 |
| 310.0 | 0.8296205577621936 | 1.0 | 0.5 | 3.5394546320186806 |
| 311.0 | 0.797111503088613 | 1.0 | 0.5 | 3.190197454292849 |
| 312.0 | 4.847906767173793e-2 | 1.0 | 0.5 | 9.93871863877833e-2 |
| 313.0 | 0.2272307227002427 | 1.0 | 0.5 | 0.5155495038419591 |
| 314.0 | 0.28798275880438495 | 1.0 | 0.5 | 0.6793063054019258 |
| 315.0 | 0.9119630135350312 | 1.0 | 0.5 | 4.859996504134525 |
| 316.0 | 0.9247506337150422 | 1.0 | 0.5 | 5.173895593701935 |
| 317.0 | 0.313064117705499 | 1.0 | 0.5 | 0.7510286422175019 |
| 318.0 | 0.10267561230063371 | 1.0 | 0.5 | 0.2166756921335689 |
| 319.0 | 0.7800056454356652 | 1.0 | 0.5 | 3.0283067880604637 |
| 320.0 | 0.9880505590690435 | 1.0 | 0.5 | 8.854141571438799 |
| 321.0 | 0.5199897620784429 | 1.0 | 0.5 | 1.4678956926088331 |
| 322.0 | 0.9631286266437556 | 1.0 | 0.5 | 6.6006396376399366 |
| 323.0 | 0.6526508389996869 | 1.0 | 0.5 | 2.1148495545527686 |
| 324.0 | 0.44331946385558707 | 1.0 | 0.5 | 1.1715274946406817 |
| 325.0 | 0.7657175467075138 | 1.0 | 0.5 | 2.9024556523792184 |
| 326.0 | 0.8587810198761535 | 1.0 | 0.5 | 3.914887085632034 |
| 327.0 | 0.17717500338441594 | 1.0 | 0.5 | 0.39002348344727783 |
| 328.0 | 0.6400146315652561 | 1.0 | 0.5 | 2.043383783189525 |
| 329.0 | 0.5429950331288752 | 1.0 | 0.5 | 1.5661220394400488 |
| 330.0 | 0.1519419896098827 | 1.0 | 0.5 | 0.3296124741021507 |
| 331.0 | 0.2317505808062127 | 1.0 | 0.5 | 0.5272816679676412 |
| 332.0 | 0.9587236527128663 | 1.0 | 0.5 | 6.374931295970532 |
| 333.0 | 0.3957876337227971 | 1.0 | 0.5 | 1.0076590860947048 |
| 334.0 | 0.7520930392631228 | 1.0 | 0.5 | 2.7894035230513716 |
| 335.0 | 0.5219702075466132 | 1.0 | 0.5 | 1.4761644422492128 |
| 336.0 | 0.2718748554726389 | 1.0 | 0.5 | 0.6345646874711566 |
| 337.0 | 0.43339022569610974 | 1.0 | 0.5 | 1.1361688818668743 |
| 338.0 | 0.654264591604089 | 1.0 | 0.5 | 2.124163024174615 |
| 339.0 | 0.21929573754620202 | 1.0 | 0.5 | 0.4951177321724933 |
| 340.0 | 0.6463525037251117 | 1.0 | 0.5 | 2.0789092703893584 |
| 341.0 | 0.40363690799123597 | 1.0 | 0.5 | 1.0338111652659434 |
| 342.0 | 0.5464987531537895 | 1.0 | 0.5 | 1.5815145200909522 |
| 343.0 | 0.2794928368714429 | 1.0 | 0.5 | 0.6555998434128101 |
| 344.0 | 0.9679988160115437 | 1.0 | 0.5 | 6.883964754455241 |
| 345.0 | 0.9374750976583118 | 1.0 | 0.5 | 5.5443807282558 |
| 346.0 | 0.5043382898350185 | 1.0 | 0.5 | 1.403723241814482 |
| 347.0 | 0.37940066231801983 | 1.0 | 0.5 | 0.9541391883810038 |
| 348.0 | 0.16327499528946765 | 1.0 | 0.5 | 0.35651962241911506 |
| 349.0 | 0.6814882638496493 | 1.0 | 0.5 | 2.2881919129060675 |
| 350.0 | 0.7593963453274154 | 1.0 | 0.5 | 2.8492085714581132 |
| 351.0 | 8.2716537875273e-2 | 1.0 | 0.5 | 0.17267747098244784 |
| 352.0 | 0.9864630812678177 | 1.0 | 0.5 | 8.604669210362268 |
| 353.0 | 0.10362777838201431 | 1.0 | 0.5 | 0.2187990527194605 |
| 354.0 | 0.3018065167087475 | 1.0 | 0.5 | 0.7185180358786611 |
| 355.0 | 0.1981827289480086 | 1.0 | 0.5 | 0.4417490773130338 |
| 356.0 | 0.2519352721846343 | 1.0 | 0.5 | 0.5805315404780675 |
| 357.0 | 0.37720491978724313 | 1.0 | 0.5 | 0.947075477038406 |
| 358.0 | 3.334532342207808e-2 | 1.0 | 0.5 | 6.782791058537331e-2 |
| 359.0 | 0.49142274606532255 | 1.0 | 0.5 | 1.3522762997820723 |
| 360.0 | 0.7658697744126436 | 1.0 | 0.5 | 2.9037555976399063 |
| 361.0 | 0.430584327777621 | 1.0 | 0.5 | 1.1262891608345273 |
| 362.0 | 0.18774246417057594 | 1.0 | 0.5 | 0.41587565350859623 |
| 363.0 | 0.43213770208564395 | 1.0 | 0.5 | 1.131752645804099 |
| 364.0 | 0.5254311249185892 | 1.0 | 0.5 | 1.4906970370036838 |
| 365.0 | 0.21632961088151903 | 1.0 | 0.5 | 0.48753353815321687 |
| 366.0 | 0.5866314734106438 | 1.0 | 0.5 | 1.766831535403086 |
| 367.0 | 0.2127018826329221 | 1.0 | 0.5 | 0.47829660009371444 |
| 368.0 | 0.762880957964817 | 1.0 | 0.5 | 2.8783859537652954 |
| 369.0 | 0.7092177159493649 | 1.0 | 0.5 | 2.4703609132000657 |
| 370.0 | 0.4668447454243504 | 1.0 | 0.5 | 1.25788522569854 |
| 371.0 | 8.984189541128584e-2 | 1.0 | 0.5 | 0.18827390651246967 |
| 372.0 | 0.8401841637115331 | 1.0 | 0.5 | 3.6674662997626886 |
| 373.0 | 0.1769541258232804 | 1.0 | 0.5 | 0.3894866793361383 |
| 374.0 | 0.32759234424821526 | 1.0 | 0.5 | 0.793780983603854 |
| 375.0 | 0.7549436630804378 | 1.0 | 0.5 | 2.812534296521244 |
| 376.0 | 0.5406091390043264 | 1.0 | 0.5 | 1.5557077645471398 |
| 377.0 | 7.226167164454322e-2 | 1.0 | 0.5 | 0.15001111942581233 |
| 378.0 | 3.9174989741246e-2 | 1.0 | 0.5 | 7.992595578900699e-2 |
| 379.0 | 0.43530038855865494 | 1.0 | 0.5 | 1.1429227007667369 |
| 380.0 | 0.19425051459560916 | 1.0 | 0.5 | 0.4319647938819565 |
| 381.0 | 0.2690512431721227 | 1.0 | 0.5 | 0.6268238435762967 |
| 382.0 | 0.6122531354155542 | 1.0 | 0.5 | 1.894805126272543 |
| 383.0 | 0.8144904991435312 | 1.0 | 0.5 | 3.3692983613822673 |
| 384.0 | 0.44435200950498954 | 1.0 | 0.5 | 1.1752405917023965 |
| 385.0 | 0.40334065577595324 | 1.0 | 0.5 | 1.0328178822819034 |
| 386.0 | 0.6712465016160815 | 1.0 | 0.5 | 2.2248941081495746 |
| 387.0 | 0.610470870145168 | 1.0 | 0.5 | 1.8856332573010395 |
| 388.0 | 0.3753417849616416 | 1.0 | 0.5 | 0.941101269529212 |
| 389.0 | 0.5760552709957159 | 1.0 | 0.5 | 1.7163043767384967 |
| 390.0 | 0.8958499208801377 | 1.0 | 0.5 | 4.523844703206438 |
| 391.0 | 0.6375933030298327 | 1.0 | 0.5 | 2.0299764509716054 |
| 392.0 | 0.4996734547364764 | 1.0 | 0.5 | 1.3849886064074164 |
| 393.0 | 0.43886175915693537 | 1.0 | 0.5 | 1.1555759704008648 |
| 394.0 | 2.1262190388847357e-2 | 1.0 | 0.5 | 4.298297362707392e-2 |
| 395.0 | 0.2612612193574123 | 1.0 | 0.5 | 0.6056217946491596 |
| 396.0 | 0.9053544127651678 | 1.0 | 0.5 | 4.715232048676575 |
| 397.0 | 5.715293130681975e-3 | 1.0 | 0.5 | 1.146337583128797e-2 |
| 398.0 | 0.2947471489241186 | 1.0 | 0.5 | 0.6983977729250015 |
| 399.0 | 8.460986179878727e-2 | 1.0 | 0.5 | 0.17680984806639513 |
| 400.0 | 0.30069220409765574 | 1.0 | 0.5 | 0.7153285923659113 |
| 401.0 | 0.997746334354857 | 1.0 | 0.5 | 12.190394425627364 |
| 402.0 | 0.18308241899251243 | 1.0 | 0.5 | 0.40443413850132504 |
| 403.0 | 0.3105228234689785 | 1.0 | 0.5 | 0.7436433675489958 |
| 404.0 | 0.9179013483983409 | 1.0 | 0.5 | 4.999667373023489 |
| 405.0 | 0.11926274283498617 | 1.0 | 0.5 | 0.2539918600578725 |
| 406.0 | 0.43776630003690786 | 1.0 | 0.5 | 1.1516753585822221 |
| 407.0 | 0.4762249664302428 | 1.0 | 0.5 | 1.2933860241922412 |
| 408.0 | 0.8482098508688078 | 1.0 | 0.5 | 3.770512619720418 |
| 409.0 | 0.4567392533274949 | 1.0 | 0.5 | 1.2203317557106708 |
| 410.0 | 0.4435608409662263 | 1.0 | 0.5 | 1.1723948842501484 |
| 411.0 | 0.50408147116895 | 1.0 | 0.5 | 1.4026872442755594 |
| 412.0 | 0.36104698819880265 | 1.0 | 0.5 | 0.8958487225315878 |
| 413.0 | 0.4194713065185254 | 1.0 | 0.5 | 1.0876321003142069 |
| 414.0 | 7.486133824518293e-2 | 1.0 | 0.5 | 0.1556232962089628 |
| 415.0 | 3.373382017468085e-2 | 1.0 | 0.5 | 6.863186850542395e-2 |
| 416.0 | 0.4932147269781292 | 1.0 | 0.5 | 1.3593357794290557 |
| 417.0 | 0.8616586394474591 | 1.0 | 0.5 | 3.956062042024912 |
| 418.0 | 0.4117184281160471 | 1.0 | 0.5 | 1.0610991639078917 |
| 419.0 | 9.116459643129304e-2 | 1.0 | 0.5 | 0.19118255076481538 |
| 420.0 | 0.5890322019489482 | 1.0 | 0.5 | 1.7784808355923856 |
| 421.0 | 0.43778596825837024 | 1.0 | 0.5 | 1.1517453243828897 |
| 422.0 | 0.5541973559190224 | 1.0 | 0.5 | 1.6157578539111792 |
| 423.0 | 0.5236632829365838 | 1.0 | 0.5 | 1.4832605720870944 |
| 424.0 | 0.6204546477584022 | 1.0 | 0.5 | 1.9375623680773675 |
| 425.0 | 0.970316394356556 | 1.0 | 0.5 | 7.034320768236008 |
| 426.0 | 0.43993687089356626 | 1.0 | 0.5 | 1.1594115421186992 |
| 427.0 | 0.8405879195529187 | 1.0 | 0.5 | 3.6725254570044403 |
| 428.0 | 0.749280638128116 | 1.0 | 0.5 | 2.766842091120008 |
| 429.0 | 0.5488427514547121 | 1.0 | 0.5 | 1.5918786677038552 |
| 430.0 | 0.13747140435906435 | 1.0 | 0.5 | 0.29577395251982774 |
| 431.0 | 0.4576343120245375 | 1.0 | 0.5 | 1.2236296079783713 |
| 432.0 | 0.8148665411500688 | 1.0 | 0.5 | 3.3733566295966297 |
| 433.0 | 0.19547202902357552 | 1.0 | 0.5 | 0.4349990900088173 |
| 434.0 | 0.4890095869756548 | 1.0 | 0.5 | 1.3428089003184602 |
| 435.0 | 0.12715136653541503 | 1.0 | 0.5 | 0.27198624962848195 |
| 436.0 | 0.2032311331013058 | 1.0 | 0.5 | 0.45438129228481317 |
| 437.0 | 0.8787527473448604 | 1.0 | 0.5 | 4.21984681587113 |
| 438.0 | 0.4663010675042861 | 1.0 | 0.5 | 1.2558467916792808 |
| 439.0 | 0.3142004342512348 | 1.0 | 0.5 | 0.7543397443148356 |
| 440.0 | 0.5376680285543044 | 1.0 | 0.5 | 1.5429441860463613 |
| 441.0 | 0.8288823435429962 | 1.0 | 0.5 | 3.530807819241641 |
| 442.0 | 0.5381362365823195 | 1.0 | 0.5 | 1.5449706315293206 |
| 443.0 | 0.8741054855972965 | 1.0 | 0.5 | 4.144621819895548 |
| 444.0 | 0.9650736097727945 | 1.0 | 0.5 | 6.709025137110639 |
| 445.0 | 8.401495565683514e-2 | 1.0 | 0.5 | 0.17551048315515766 |
| 446.0 | 0.16014272925069362 | 1.0 | 0.5 | 0.34904663471346736 |
| 447.0 | 0.6761260538348454 | 1.0 | 0.5 | 2.254801787874353 |
| 448.0 | 0.30491746315485757 | 1.0 | 0.5 | 0.7274493648359186 |
| 449.0 | 0.8823536465498564 | 1.0 | 0.5 | 4.280144318376048 |
| 450.0 | 0.35138375129442345 | 1.0 | 0.5 | 0.8658280669121068 |
| 451.0 | 0.8162562408450885 | 1.0 | 0.5 | 3.388426210497536 |
| 452.0 | 0.673617698352461 | 1.0 | 0.5 | 2.2393717605117374 |
| 453.0 | 8.256976107323988e-2 | 1.0 | 0.5 | 0.1723574716228483 |
| 454.0 | 0.8365768515057421 | 1.0 | 0.5 | 3.6228248778772634 |
| 455.0 | 0.48971308441437955 | 1.0 | 0.5 | 1.3455642637453662 |
| 456.0 | 8.774182190296687e-2 | 1.0 | 0.5 | 0.18366447790309529 |
| 457.0 | 0.24099238723353444 | 1.0 | 0.5 | 0.5514869432829278 |
| 458.0 | 0.5907862348164701 | 1.0 | 0.5 | 1.787035212429029 |
| 459.0 | 0.9282830732565702 | 1.0 | 0.5 | 5.270056963848408 |
| 460.0 | 0.32874248016649343 | 1.0 | 0.5 | 0.7972048609806637 |
| 461.0 | 0.7759823021398251 | 1.0 | 0.5 | 2.9920604438874734 |
| 462.0 | 0.9906880008613337 | 1.0 | 0.5 | 9.352902960980467 |
| 463.0 | 0.35519401347986057 | 1.0 | 0.5 | 0.8776116070547805 |
| 464.0 | 0.22036578800170115 | 1.0 | 0.5 | 0.4978608565413814 |
| 465.0 | 0.8023870524533302 | 1.0 | 0.5 | 3.242889943586732 |
| 466.0 | 0.6555700243159146 | 1.0 | 0.5 | 2.131728945611721 |
| 467.0 | 0.6966117625572236 | 1.0 | 0.5 | 2.385483963919468 |
| 468.0 | 0.31199672354331665 | 1.0 | 0.5 | 0.7479233575368842 |
| 469.0 | 0.10874684309305482 | 1.0 | 0.5 | 0.23025353029665035 |
| 470.0 | 0.7768279542152332 | 1.0 | 0.5 | 2.999624598436637 |
| 471.0 | 0.5194659966765868 | 1.0 | 0.5 | 1.465714573067321 |
| 472.0 | 3.387321287945699e-2 | 1.0 | 0.5 | 6.892040754981558e-2 |
| 473.0 | 0.9137616097231142 | 1.0 | 0.5 | 4.901279675232361 |
| 474.0 | 0.9310800753319032 | 1.0 | 0.5 | 5.349619920714008 |
| 475.0 | 0.8956753753595035 | 1.0 | 0.5 | 4.520495700986587 |
| 476.0 | 0.9734569283996768 | 1.0 | 0.5 | 7.257973044045264 |
| 477.0 | 0.3100378260015375 | 1.0 | 0.5 | 0.7422370063712049 |
| 478.0 | 0.9004067891568347 | 1.0 | 0.5 | 4.613322561880238 |
| 479.0 | 0.3595142946826074 | 1.0 | 0.5 | 0.8910569518000615 |
| 480.0 | 0.5858555389814262 | 1.0 | 0.5 | 1.7630808527274318 |
| 481.0 | 0.7207027332090898 | 1.0 | 0.5 | 2.5509571840094765 |
| 482.0 | 0.5207773901843563 | 1.0 | 0.5 | 1.4711801017479904 |
| 483.0 | 0.5010641338355079 | 1.0 | 0.5 | 1.3905554324221692 |
| 484.0 | 0.7386177898402442 | 1.0 | 0.5 | 2.683543072229971 |
| 485.0 | 0.7514865091276058 | 1.0 | 0.5 | 2.784516291388268 |
| 486.0 | 0.996791280204512 | 1.0 | 0.5 | 11.48376647798819 |
| 487.0 | 0.31973896223026976 | 1.0 | 0.5 | 0.7705573508031445 |
| 488.0 | 0.5409498257000886 | 1.0 | 0.5 | 1.557191525390985 |
| 489.0 | 0.2351915468213719 | 1.0 | 0.5 | 0.5362597290194078 |
| 490.0 | 0.5722823786456511 | 1.0 | 0.5 | 1.6985841286612482 |
| 491.0 | 0.8872393295593034 | 1.0 | 0.5 | 4.364975334044577 |
| 492.0 | 0.7466377111677412 | 1.0 | 0.5 | 2.745869685758634 |
| 493.0 | 0.8944045014489479 | 1.0 | 0.5 | 4.496279071952017 |
| 494.0 | 0.7761279146229962 | 1.0 | 0.5 | 2.993360875321733 |
| 495.0 | 6.282087728304298e-4 | 1.0 | 0.5 | 1.2568123572811955e-3 |
| 496.0 | 2.1755806290161828e-2 | 1.0 | 0.5 | 4.399190658802096e-2 |
| 497.0 | 0.25822043228050306 | 1.0 | 0.5 | 0.5974063169929631 |
| 498.0 | 0.36706789505445514 | 1.0 | 0.5 | 0.9147842435207847 |
| 499.0 | 0.8535706465084604 | 1.0 | 0.5 | 3.8424243911227456 |
| 500.0 | 0.9583607379261688 | 1.0 | 0.5 | 6.357423513924509 |
| 501.0 | 0.1736332737734787 | 1.0 | 0.5 | 0.38143325101594394 |
| 502.0 | 0.7191057722209122 | 1.0 | 0.5 | 2.539554188214649 |
| 503.0 | 0.5866772207662699 | 1.0 | 0.5 | 1.7670528869782172 |
| 504.0 | 0.44342504955089734 | 1.0 | 0.5 | 1.171906870972165 |
| 505.0 | 0.8638825079861409 | 1.0 | 0.5 | 3.988473708673089 |
| 506.0 | 0.19206484030065873 | 1.0 | 0.5 | 0.42654694315586256 |
| 507.0 | 6.329398370441452e-2 | 1.0 | 0.5 | 0.1307715918497558 |
| 508.0 | 0.8324305296152746 | 1.0 | 0.5 | 3.5727145302718077 |
| 509.0 | 0.788341172013531 | 1.0 | 0.5 | 3.105559205156182 |
| 510.0 | 0.9968069620813552 | 1.0 | 0.5 | 11.493564979531474 |
| 511.0 | 0.5738675981141458 | 1.0 | 0.5 | 1.70601035690584 |
| 512.0 | 2.1924901743684888e-2 | 1.0 | 0.5 | 4.433764862438196e-2 |
| 513.0 | 0.31475857342097047 | 1.0 | 0.5 | 0.755968110508673 |
| 514.0 | 0.5840924030390661 | 1.0 | 0.5 | 1.7545843321676697 |
| 515.0 | 0.7770937124350712 | 1.0 | 0.5 | 3.0020076619607927 |
| 516.0 | 0.7130309607190969 | 1.0 | 0.5 | 2.496761892223379 |
| 517.0 | 0.3991683439699586 | 1.0 | 0.5 | 1.0188809802465286 |
| 518.0 | 0.48509787688675254 | 1.0 | 0.5 | 1.3275568971757934 |
| 519.0 | 0.45169086393350866 | 1.0 | 0.5 | 1.2018320683571915 |
| 520.0 | 0.12902719463817103 | 1.0 | 0.5 | 0.2762890498682689 |
| 521.0 | 0.2592134704552379 | 1.0 | 0.5 | 0.6000855589507411 |
| 522.0 | 0.2813001484016546 | 1.0 | 0.5 | 0.660622921987986 |
| 523.0 | 0.3762566475369137 | 1.0 | 0.5 | 0.9440325786940167 |
| 524.0 | 0.9267694189625785 | 1.0 | 0.5 | 5.228284343045507 |
| 525.0 | 0.15197347800388006 | 1.0 | 0.5 | 0.32968673548099053 |
| 526.0 | 0.14593597548755854 | 1.0 | 0.5 | 0.3154982356972103 |
| 527.0 | 0.35991105880863383 | 1.0 | 0.5 | 0.8922962833448616 |
| 528.0 | 0.3071108301936344 | 1.0 | 0.5 | 0.7337704414228866 |
| 529.0 | 0.18406488587857806 | 1.0 | 0.5 | 0.40684088837547255 |
| 530.0 | 0.5136711150110314 | 1.0 | 0.5 | 1.4417403317355608 |
| 531.0 | 0.6056599490894785 | 1.0 | 0.5 | 1.8610833370818218 |
| 532.0 | 0.44302266378392197 | 1.0 | 0.5 | 1.1704614578045645 |
| 533.0 | 0.7453946147476727 | 1.0 | 0.5 | 2.736080882533261 |
| 534.0 | 0.35502683979054106 | 1.0 | 0.5 | 0.8770931502610587 |
| 535.0 | 0.8797839338461947 | 1.0 | 0.5 | 4.236929207935352 |
| 536.0 | 6.238712029644411e-2 | 1.0 | 0.5 | 0.12883624673709432 |
| 537.0 | 0.9424758304767943 | 1.0 | 0.5 | 5.711100159925468 |
| 538.0 | 0.5704103694439081 | 1.0 | 0.5 | 1.689849747036119 |
| 539.0 | 0.9158448954522559 | 1.0 | 0.5 | 4.950187400162322 |
| 540.0 | 0.8873811703242918 | 1.0 | 0.5 | 4.367492702015337 |
| 541.0 | 0.6216382152888731 | 1.0 | 0.5 | 1.9438088773652653 |
| 542.0 | 0.48827370203072595 | 1.0 | 0.5 | 1.3399307423143256 |
| 543.0 | 0.4572473011375716 | 1.0 | 0.5 | 1.2222029953319737 |
| 544.0 | 3.967393021768029e-2 | 1.0 | 0.5 | 8.096479233410842e-2 |
| 545.0 | 0.29505351687188286 | 1.0 | 0.5 | 0.6992667790155687 |
| 546.0 | 0.5639878272499856 | 1.0 | 0.5 | 1.6601702337428519 |
| 547.0 | 0.48965142848521204 | 1.0 | 0.5 | 1.3453226263340796 |
| 548.0 | 0.8749613569725964 | 1.0 | 0.5 | 4.15826489047167 |
| 549.0 | 0.9256239598413039 | 1.0 | 0.5 | 5.19724285977426 |
| 550.0 | 0.7569605924580269 | 1.0 | 0.5 | 2.8290633556705096 |
| 551.0 | 0.3844172120051691 | 1.0 | 0.5 | 0.9703716742615802 |
| 552.0 | 0.1362858511639865 | 1.0 | 0.5 | 0.29302682234878386 |
| 553.0 | 0.37983323803435165 | 1.0 | 0.5 | 0.9555337323937093 |
| 554.0 | 0.26566735487098103 | 1.0 | 0.5 | 0.6175863160615908 |
| 555.0 | 0.36961293921650573 | 1.0 | 0.5 | 0.9228425321120206 |
| 556.0 | 9.63913755811967e-2 | 1.0 | 0.5 | 0.2027178998486325 |
| 557.0 | 0.18788147800222665 | 1.0 | 0.5 | 0.4162179728411676 |
| 558.0 | 0.26504942094508055 | 1.0 | 0.5 | 0.6159040428220617 |
| 559.0 | 0.739578089934357 | 1.0 | 0.5 | 2.6909044643005315 |
| 560.0 | 0.6457524310661318 | 1.0 | 0.5 | 2.075518526013794 |
| 561.0 | 0.9092071659989257 | 1.0 | 0.5 | 4.798349834364611 |
| 562.0 | 0.210969342425946 | 1.0 | 0.5 | 0.4739002053005348 |
| 563.0 | 0.9875371965277739 | 1.0 | 0.5 | 8.770013586320161 |
| 564.0 | 5.026001823606596e-2 | 1.0 | 0.5 | 0.10313407051509839 |
| 565.0 | 0.12100530799389475 | 1.0 | 0.5 | 0.2579528399771316 |
| 566.0 | 0.5890018173984741 | 1.0 | 0.5 | 1.7783329727795534 |
| 567.0 | 0.7224532694248171 | 1.0 | 0.5 | 2.563531923007902 |
| 568.0 | 0.8883622267112601 | 1.0 | 0.5 | 4.384991631947751 |
| 569.0 | 1.997234871111797e-2 | 1.0 | 0.5 | 4.0348984229344e-2 |
| 570.0 | 0.2212019516532856 | 1.0 | 0.5 | 0.5000070229243507 |
| 571.0 | 0.4273320266042193 | 1.0 | 0.5 | 1.1148983665533385 |
| 572.0 | 0.9981606891187609 | 1.0 | 0.5 | 12.596728597154938 |
| 573.0 | 0.24173972097342367 | 1.0 | 0.5 | 0.5534571525106269 |
| 574.0 | 0.30996923292210077 | 1.0 | 0.5 | 0.7420381848339302 |
| 575.0 | 0.5586292644735815 | 1.0 | 0.5 | 1.635740173145147 |
| 576.0 | 0.5710479605847417 | 1.0 | 0.5 | 1.6928203250772282 |
| 577.0 | 0.7716668116970227 | 1.0 | 0.5 | 2.953898729115331 |
| 578.0 | 0.1347753271289409 | 1.0 | 0.5 | 0.2895321367059855 |
| 579.0 | 0.7338594799945437 | 1.0 | 0.5 | 2.647461677978153 |
| 580.0 | 0.11653928663743118 | 1.0 | 0.5 | 0.2478169105193333 |
| 581.0 | 0.939970706008818 | 1.0 | 0.5 | 5.6258452054414265 |
| 582.0 | 0.6523955046706064 | 1.0 | 0.5 | 2.1133799063928125 |
| 583.0 | 0.8032152138335183 | 1.0 | 0.5 | 3.2512892068337553 |
| 584.0 | 0.6707576127357098 | 1.0 | 0.5 | 2.22192212014176 |
| 585.0 | 0.706492182007378 | 1.0 | 0.5 | 2.451702005911791 |
| 586.0 | 0.7647771000645677 | 1.0 | 0.5 | 2.894443408052288 |
| 587.0 | 0.8173666289040515 | 1.0 | 0.5 | 3.400549144677044 |
| 588.0 | 0.21872300084295448 | 1.0 | 0.5 | 0.49365103921530656 |
| 589.0 | 0.7310001086753175 | 1.0 | 0.5 | 2.626088606755685 |
| 590.0 | 0.8697372961374548 | 1.0 | 0.5 | 4.076404137303028 |
| 591.0 | 0.16400157324713815 | 1.0 | 0.5 | 0.35825709554754664 |
| 592.0 | 0.443313209241608 | 1.0 | 0.5 | 1.1715050236598006 |
| 593.0 | 0.8053657385685586 | 1.0 | 0.5 | 3.2732661278566844 |
| 594.0 | 0.30079618388942353 | 1.0 | 0.5 | 0.7156259936633084 |
| 595.0 | 0.45365879994299696 | 1.0 | 0.5 | 1.2090231797639022 |
| 596.0 | 0.8069809893677921 | 1.0 | 0.5 | 3.2899331884862204 |
| 597.0 | 0.8245884511775761 | 1.0 | 0.5 | 3.4812407168767363 |
| 598.0 | 0.41234621682526684 | 1.0 | 0.5 | 1.063234617242673 |
| 599.0 | 0.7968250346184004 | 1.0 | 0.5 | 3.1873755454599793 |
| 600.0 | 0.3927417451741644 | 1.0 | 0.5 | 0.9976022348148476 |
| 601.0 | 0.2728256265562098 | 1.0 | 0.5 | 0.6371779535572102 |
| 602.0 | 8.203703710226784e-2 | 1.0 | 0.5 | 0.1711964692057045 |
| 603.0 | 0.37776869105901545 | 1.0 | 0.5 | 0.9488867520931888 |
| 604.0 | 0.629411417011517 | 1.0 | 0.5 | 1.9853255448725573 |
| 605.0 | 0.7501910378551001 | 1.0 | 0.5 | 2.774117609305619 |
| 606.0 | 0.5996333900313177 | 1.0 | 0.5 | 1.8307492534099181 |
| 607.0 | 6.970492186242305e-2 | 1.0 | 0.5 | 0.14450690968030883 |
| 608.0 | 0.8940723635978487 | 1.0 | 0.5 | 4.489998186900931 |
| 609.0 | 0.6914380476986998 | 1.0 | 0.5 | 2.3516652759309613 |
| 610.0 | 0.3650550811756098 | 1.0 | 0.5 | 0.9084340517211708 |
| 611.0 | 6.128389132423373e-2 | 1.0 | 0.5 | 0.12648435832908939 |
| 612.0 | 0.7680345331232672 | 1.0 | 0.5 | 2.9223335361285874 |
| 613.0 | 0.23058140936561866 | 1.0 | 0.5 | 0.5242402528938145 |
| 614.0 | 0.18527936511039267 | 1.0 | 0.5 | 0.40982000756014936 |
| 615.0 | 0.28710359124883345 | 1.0 | 0.5 | 0.676838316784801 |
| 616.0 | 0.2108511727487865 | 1.0 | 0.5 | 0.4736006964588179 |
| 617.0 | 0.4624966724507077 | 1.0 | 0.5 | 1.241640656420202 |
| 618.0 | 0.9811961480020145 | 1.0 | 0.5 | 7.947387073248349 |
| 619.0 | 0.8245811182215507 | 1.0 | 0.5 | 3.4811571100355176 |
| 620.0 | 0.8168888824501829 | 1.0 | 0.5 | 3.3953242213722077 |
| 621.0 | 0.6208021859846258 | 1.0 | 0.5 | 1.9393945466905502 |
| 622.0 | 0.6514815007857316 | 1.0 | 0.5 | 2.108127935592991 |
| 623.0 | 0.8002695403068458 | 1.0 | 0.5 | 3.221573045869622 |
| 624.0 | 0.44461502006124476 | 1.0 | 0.5 | 1.176187496315193 |
| 625.0 | 0.24944701714221795 | 1.0 | 0.5 | 0.5738900673091492 |
| 626.0 | 0.15074825367641687 | 1.0 | 0.5 | 0.32679923126166255 |
| 627.0 | 0.48159464793185214 | 1.0 | 0.5 | 1.3139956195836684 |
| 628.0 | 0.7293846186875164 | 1.0 | 0.5 | 2.614113446702638 |
| 629.0 | 0.22027782974542087 | 1.0 | 0.5 | 0.49763522946247984 |
| 630.0 | 0.20798244662851184 | 1.0 | 0.5 | 0.46634344813095197 |
| 631.0 | 0.6919106866218325 | 1.0 | 0.5 | 2.354731119087316 |
| 632.0 | 0.9720879966592072 | 1.0 | 0.5 | 7.1573969108149464 |
| 633.0 | 0.9555225762947214 | 1.0 | 0.5 | 6.2255471020245 |
| 634.0 | 0.9266176357074408 | 1.0 | 0.5 | 5.22414328144902 |
| 635.0 | 0.3345233531787639 | 1.0 | 0.5 | 0.8145034658807747 |
| 636.0 | 0.718429616730576 | 1.0 | 0.5 | 2.5347456665564865 |
| 637.0 | 0.8878295863454977 | 1.0 | 0.5 | 4.375472027171012 |
| 638.0 | 0.3092083755044568 | 1.0 | 0.5 | 0.7398341142762942 |
| 639.0 | 0.7848160471899103 | 1.0 | 0.5 | 3.0725240444024844 |
| 640.0 | 0.4053789118552892 | 1.0 | 0.5 | 1.0396618058885414 |
| 641.0 | 0.7411589342475445 | 1.0 | 0.5 | 2.7030821027672483 |
| 642.0 | 0.45881127046875336 | 1.0 | 0.5 | 1.2279744157261765 |
| 643.0 | 0.22682375665456067 | 1.0 | 0.5 | 0.5144965144587033 |
| 644.0 | 0.16776368155499566 | 1.0 | 0.5 | 0.3672776837982627 |
| 645.0 | 0.5154869010668474 | 1.0 | 0.5 | 1.449221624070085 |
| 646.0 | 0.34454745235530915 | 1.0 | 0.5 | 0.8448587389647154 |
| 647.0 | 0.7272962306454143 | 1.0 | 0.5 | 2.5987383337541456 |
| 648.0 | 0.9326344100560882 | 1.0 | 0.5 | 5.395241852776035 |
| 649.0 | 0.29777872274027883 | 1.0 | 0.5 | 0.7070134297056129 |
| 650.0 | 0.9174126215561729 | 1.0 | 0.5 | 4.987796826948644 |
| 651.0 | 4.5376575536940744e-2 | 1.0 | 0.5 | 9.28766723998495e-2 |
| 652.0 | 0.4810458793989424 | 1.0 | 0.5 | 1.3118795986726604 |
| 653.0 | 0.2950958156948137 | 1.0 | 0.5 | 0.6993867883865484 |
| 654.0 | 0.2212183176044329 | 1.0 | 0.5 | 0.5000490521080256 |
| 655.0 | 0.8206110378697538 | 1.0 | 0.5 | 3.436397715675071 |
| 656.0 | 0.5629205916000906 | 1.0 | 0.5 | 1.6552807756173478 |
| 657.0 | 0.6994457934362445 | 1.0 | 0.5 | 2.4042543067509254 |
| 658.0 | 0.9830737705685453 | 1.0 | 0.5 | 8.15778164570834 |
| 659.0 | 0.8325491011982105 | 1.0 | 0.5 | 3.5741302243464643 |
| 660.0 | 0.2556320014497683 | 1.0 | 0.5 | 0.5904394894534792 |
| 661.0 | 0.5735719811333211 | 1.0 | 0.5 | 1.7046233960032402 |
| 662.0 | 0.3675775710486384 | 1.0 | 0.5 | 0.916395415751471 |
| 663.0 | 0.34003954250576196 | 1.0 | 0.5 | 0.8311507172880653 |
| 664.0 | 0.11876686109163148 | 1.0 | 0.5 | 0.2528661163846483 |
| 665.0 | 0.4006303818268926 | 1.0 | 0.5 | 1.0237536248988528 |
| 666.0 | 0.6505245015357007 | 1.0 | 0.5 | 2.10264364860553 |
| 667.0 | 0.10086413330967303 | 1.0 | 0.5 | 0.21264225003430867 |
| 668.0 | 0.5732670526062165 | 1.0 | 0.5 | 1.7031937546945186 |
| 669.0 | 0.11878163120957308 | 1.0 | 0.5 | 0.25289963814199007 |
| 670.0 | 0.10072655638961314 | 1.0 | 0.5 | 0.21233625313017732 |
| 671.0 | 0.5618155656093103 | 1.0 | 0.5 | 1.6502307483050531 |
| 672.0 | 0.4500194541428453 | 1.0 | 0.5 | 1.1957447451000092 |
| 673.0 | 0.3981179573374246 | 1.0 | 0.5 | 1.0153875905870227 |
| 674.0 | 0.3056380605496083 | 1.0 | 0.5 | 0.7295238556749573 |
| 675.0 | 0.30445908406220024 | 1.0 | 0.5 | 0.7261308796389632 |
| 676.0 | 0.3632627258496999 | 1.0 | 0.5 | 0.9027963019039356 |
| 677.0 | 0.9360322656138116 | 1.0 | 0.5 | 5.49875294592944 |
| 678.0 | 9.829392285480298e-2 | 1.0 | 0.5 | 0.20693333769178202 |
| 679.0 | 0.16857186470900742 | 1.0 | 0.5 | 0.3692208237524054 |
| 680.0 | 0.6851865756088779 | 1.0 | 0.5 | 2.3115502383155535 |
| 681.0 | 6.0746870332459846e-2 | 1.0 | 0.5 | 0.12534052488294534 |
| 682.0 | 0.13633830378369793 | 1.0 | 0.5 | 0.29314828432216455 |
| 683.0 | 7.392456123744273e-2 | 1.0 | 0.5 | 0.15359916061613507 |
| 684.0 | 0.5086610045902572 | 1.0 | 0.5 | 1.4212419421338762 |
| 685.0 | 6.49975666739836e-2 | 1.0 | 0.5 | 0.13441229441823688 |
| 686.0 | 0.5965150506093662 | 1.0 | 0.5 | 1.8152321842298589 |
| 687.0 | 0.4605739048163946 | 1.0 | 0.5 | 1.2344989825581794 |
| 688.0 | 6.864473076963751e-2 | 1.0 | 0.5 | 0.14222894978542783 |
| 689.0 | 0.8344360964556317 | 1.0 | 0.5 | 3.596796069113207 |
| 690.0 | 8.732363913477414e-2 | 1.0 | 0.5 | 0.18274788003816578 |
| 691.0 | 0.21142843578856352 | 1.0 | 0.5 | 0.4750642335181355 |
| 692.0 | 0.7222864812254323 | 1.0 | 0.5 | 2.56233040927018 |
| 693.0 | 0.8184225707114926 | 1.0 | 0.5 | 3.41214621719645 |
| 694.0 | 0.21034070512424763 | 1.0 | 0.5 | 0.4723073977097867 |
| 695.0 | 0.3162647360277784 | 1.0 | 0.5 | 0.7603689545111227 |
| 696.0 | 0.790802361192664 | 1.0 | 0.5 | 3.1289516672537636 |
| 697.0 | 0.13344125555833664 | 1.0 | 0.5 | 0.28645075407773934 |
| 698.0 | 0.7007503099963927 | 1.0 | 0.5 | 2.4129539409113554 |
| 699.0 | 0.9187168349404677 | 1.0 | 0.5 | 5.019632711418354 |
| 700.0 | 0.4599361344660652 | 1.0 | 0.5 | 1.2321357538196342 |
| 701.0 | 0.27682148640453996 | 1.0 | 0.5 | 0.6481983610575905 |
| 702.0 | 1.800784163218272e-3 | 1.0 | 0.5 | 3.604815048388172e-3 |
| 703.0 | 0.7228478637519237 | 1.0 | 0.5 | 2.566377390466142 |
| 704.0 | 0.2985911645520416 | 1.0 | 0.5 | 0.7093286889612735 |
| 705.0 | 0.17315276773821076 | 1.0 | 0.5 | 0.38027065243919844 |
| 706.0 | 0.14367496112886713 | 1.0 | 0.5 | 0.3102105132154968 |
| 707.0 | 0.8032824900526222 | 1.0 | 0.5 | 3.2519730780123863 |
| 708.0 | 0.9263217228455575 | 1.0 | 0.5 | 5.216094540240102 |
| 709.0 | 0.14390022398042068 | 1.0 | 0.5 | 0.3107366977250743 |
| 710.0 | 0.16040391518009045 | 1.0 | 0.5 | 0.349668708391516 |
| 711.0 | 0.23371462343815053 | 1.0 | 0.5 | 0.5324012487287254 |
| 712.0 | 0.5919728166898646 | 1.0 | 0.5 | 1.7928429620744737 |
| 713.0 | 0.103304290572939 | 1.0 | 0.5 | 0.2180774117665795 |
| 714.0 | 0.9472027518538018 | 1.0 | 0.5 | 5.8825924161427 |
| 715.0 | 2.606352915311938e-2 | 1.0 | 0.5 | 5.2818404940095244e-2 |
| 716.0 | 0.3644003382289155 | 1.0 | 0.5 | 0.9063727529217371 |
| 717.0 | 0.8078186466096073 | 1.0 | 0.5 | 3.298631607703358 |
| 718.0 | 0.945005788294678 | 1.0 | 0.5 | 5.801054682018765 |
| 719.0 | 0.3026751927307447 | 1.0 | 0.5 | 0.7210079384190151 |
| 720.0 | 0.13192709639852174 | 1.0 | 0.5 | 0.28295915504898594 |
| 721.0 | 0.2566043774869836 | 1.0 | 0.5 | 0.5930538192083015 |
| 722.0 | 0.10698594605754641 | 1.0 | 0.5 | 0.22630592066398597 |
| 723.0 | 0.6145249789812167 | 1.0 | 0.5 | 1.9065577687955066 |
| 724.0 | 8.930234303466789e-2 | 1.0 | 0.5 | 0.18708863439196044 |
| 725.0 | 0.5078727259176272 | 1.0 | 0.5 | 1.4180358175685772 |
| 726.0 | 0.6074906579312169 | 1.0 | 0.5 | 1.87038988118759 |
| 727.0 | 0.4328926795524597 | 1.0 | 0.5 | 1.1344134309597795 |
| 728.0 | 0.9199649411490993 | 1.0 | 0.5 | 5.0505810093383765 |
| 729.0 | 0.9554407909442231 | 1.0 | 0.5 | 6.221872867230832 |
| 730.0 | 0.25047456653555367 | 1.0 | 0.5 | 0.5766300562133851 |
| 731.0 | 0.8421247486490246 | 1.0 | 0.5 | 3.6919002124484552 |
| 732.0 | 7.259025420929721e-2 | 1.0 | 0.5 | 0.15071959671184512 |
| 733.0 | 0.11620186500134255 | 1.0 | 0.5 | 0.24705319299034392 |
| 734.0 | 0.30850615207588095 | 1.0 | 0.5 | 0.7378020492564494 |
| 735.0 | 0.1883626739567803 | 1.0 | 0.5 | 0.4174033627979661 |
| 736.0 | 0.6623776199438104 | 1.0 | 0.5 | 2.171654453643791 |
| 737.0 | 0.11290896034135722 | 1.0 | 0.5 | 0.2396153284366351 |
| 738.0 | 0.7422642656298815 | 1.0 | 0.5 | 2.7116410087728458 |
| 739.0 | 0.7587318490143916 | 1.0 | 0.5 | 2.8436926086943526 |
| 740.0 | 0.530336944600492 | 1.0 | 0.5 | 1.5114794895629295 |
| 741.0 | 0.6223687487469611 | 1.0 | 0.5 | 1.9476741705662395 |
| 742.0 | 0.5630377543656311 | 1.0 | 0.5 | 1.6558169640962446 |
| 743.0 | 0.6774789767908294 | 1.0 | 0.5 | 2.2631739132509616 |
| 744.0 | 0.7134140595880138 | 1.0 | 0.5 | 2.499433642548651 |
| 745.0 | 0.10002976757404214 | 1.0 | 0.5 | 0.2107871825741759 |
| 746.0 | 0.567848281252265 | 1.0 | 0.5 | 1.677957103179304 |
| 747.0 | 0.4060863958044679 | 1.0 | 0.5 | 1.0420428353605442 |
| 748.0 | 9.50513656846318e-2 | 1.0 | 0.5 | 0.19975418911112916 |
| 749.0 | 0.16631249236543122 | 1.0 | 0.5 | 0.36379327584656745 |
| 750.0 | 0.8411217147036525 | 1.0 | 0.5 | 3.6792337423769124 |
| 751.0 | 0.8261179212585096 | 1.0 | 0.5 | 3.49875583579596 |
| 752.0 | 2.270068052664842e-2 | 1.0 | 0.5 | 4.5924615942012824e-2 |
| 753.0 | 0.794409914691346 | 1.0 | 0.5 | 3.163741939541678 |
| 754.0 | 9.042111886289728e-2 | 1.0 | 0.5 | 0.18954710912519077 |
| 755.0 | 0.8703489330265705 | 1.0 | 0.5 | 4.0858170747733435 |
| 756.0 | 0.4310407173673936 | 1.0 | 0.5 | 1.1278928138744269 |
| 757.0 | 0.3363067143343986 | 1.0 | 0.5 | 0.819870310803085 |
| 758.0 | 0.5130758701244038 | 1.0 | 0.5 | 1.4392939176921202 |
| 759.0 | 0.7908192098998476 | 1.0 | 0.5 | 3.1291127530653498 |
| 760.0 | 0.13285818054236243 | 1.0 | 0.5 | 0.28510548131242014 |
| 761.0 | 0.20374760571336736 | 1.0 | 0.5 | 0.45567813029380005 |
| 762.0 | 0.5394576445781261 | 1.0 | 0.5 | 1.5507009009747217 |
| 763.0 | 6.022815649686475e-2 | 1.0 | 0.5 | 0.12423630571358278 |
| 764.0 | 6.998298713513784e-2 | 1.0 | 0.5 | 0.1451047991981602 |
| 765.0 | 0.8970007092256013 | 1.0 | 0.5 | 4.546066352923313 |
| 766.0 | 0.4229521552684008 | 1.0 | 0.5 | 1.0996601921991296 |
| 767.0 | 0.8670835830456738 | 1.0 | 0.5 | 4.036069584532981 |
| 768.0 | 0.4225403174878275 | 1.0 | 0.5 | 1.0982333057100466 |
| 769.0 | 0.7514278370027265 | 1.0 | 0.5 | 2.784044162493016 |
| 770.0 | 0.3166625910490667 | 1.0 | 0.5 | 0.7615330624342808 |
| 771.0 | 0.35844011299274825 | 1.0 | 0.5 | 0.8877054892696948 |
| 772.0 | 0.6478614003000068 | 1.0 | 0.5 | 2.08746086347037 |
| 773.0 | 0.7201698321036184 | 1.0 | 0.5 | 2.547144806123709 |
| 774.0 | 0.36834307496040686 | 1.0 | 0.5 | 0.9188177447070995 |
| 775.0 | 0.8093749686530289 | 1.0 | 0.5 | 3.3148939343546955 |
| 776.0 | 0.11152824342387613 | 1.0 | 0.5 | 0.2365048393033555 |
| 777.0 | 0.8664817488175403 | 1.0 | 0.5 | 4.027034195017947 |
| 778.0 | 0.5335069631739435 | 1.0 | 0.5 | 1.525024370448048 |
| 779.0 | 0.3938842132094552 | 1.0 | 0.5 | 1.0013684877054532 |
| 780.0 | 0.10183253557450678 | 1.0 | 0.5 | 0.21479748413816208 |
| 781.0 | 8.053929148343719e-2 | 1.0 | 0.5 | 0.16793593441701318 |
| 782.0 | 0.6987324536890543 | 1.0 | 0.5 | 2.39951310171572 |
| 783.0 | 0.9652504478720477 | 1.0 | 0.5 | 6.719177190917287 |
| 784.0 | 8.352182889441673e-2 | 1.0 | 0.5 | 0.17443405931174336 |
| 785.0 | 0.6848311037778758 | 1.0 | 0.5 | 2.309293210731784 |
| 786.0 | 0.8753831237435886 | 1.0 | 0.5 | 4.165022476660053 |
| 787.0 | 0.5017501580403889 | 1.0 | 0.5 | 1.3933072741604589 |
| 788.0 | 0.5164740417097261 | 1.0 | 0.5 | 1.4533005544713893 |
| 789.0 | 5.34146726412843e-3 | 1.0 | 0.5 | 1.0711567808793232e-2 |
| 790.0 | 0.3318023439651666 | 1.0 | 0.5 | 0.8063425138905206 |
| 791.0 | 0.8980158281288398 | 1.0 | 0.5 | 4.565875310945801 |
| 792.0 | 0.3077912787425361 | 1.0 | 0.5 | 0.7357354970569514 |
| 793.0 | 8.244870505377133e-2 | 1.0 | 0.5 | 0.1720935866356492 |
| 794.0 | 0.9525163323404254 | 1.0 | 0.5 | 6.09473893162687 |
| 795.0 | 0.8150156026591003 | 1.0 | 0.5 | 3.37496759231672 |
| 796.0 | 0.6942073379694466 | 1.0 | 0.5 | 2.3696959635020733 |
| 797.0 | 0.3064154871080699 | 1.0 | 0.5 | 0.7317643647045484 |
| 798.0 | 0.6766043921266462 | 1.0 | 0.5 | 2.257757826055945 |
| 799.0 | 0.8850082431583656 | 1.0 | 0.5 | 4.325789665651913 |
| 800.0 | 0.390824501859813 | 1.0 | 0.5 | 0.9912977570009057 |
| 801.0 | 0.9277232680571199 | 1.0 | 0.5 | 5.254506056261732 |
| 802.0 | 0.9515352841551716 | 1.0 | 0.5 | 6.0538385083107515 |
| 803.0 | 0.931339195694947 | 1.0 | 0.5 | 5.3571535534070165 |
| 804.0 | 0.41693501872311356 | 1.0 | 0.5 | 1.078913277353363 |
| 805.0 | 0.9925072396884476 | 1.0 | 0.5 | 9.787636032854072 |
| 806.0 | 0.6270345104129288 | 1.0 | 0.5 | 1.9725387696659187 |
| 807.0 | 0.9026186077418837 | 1.0 | 0.5 | 4.658240262325699 |
| 808.0 | 0.9572829405210631 | 1.0 | 0.5 | 6.306313838242294 |
| 809.0 | 0.3836448542598979 | 1.0 | 0.5 | 0.9678638925777285 |
| 810.0 | 0.9149835758010022 | 1.0 | 0.5 | 4.929821630573945 |
| 811.0 | 0.9650769640332738 | 1.0 | 0.5 | 6.70921722232391 |
| 812.0 | 0.8333633906893451 | 1.0 | 0.5 | 3.5838796592561692 |
| 813.0 | 0.4659441674707303 | 1.0 | 0.5 | 1.254509780378417 |
| 814.0 | 0.6644565585399186 | 1.0 | 0.5 | 2.1840076964202977 |
| 815.0 | 0.5950530445808047 | 1.0 | 0.5 | 1.8079983894541376 |
| 816.0 | 0.6963681256453612 | 1.0 | 0.5 | 2.383878501957269 |
| 817.0 | 0.7577963228381738 | 1.0 | 0.5 | 2.835952531284403 |
| 818.0 | 5.205609640929987e-2 | 1.0 | 0.5 | 0.10691990381092617 |
| 819.0 | 0.266407912351019 | 1.0 | 0.5 | 0.6196042874911304 |
| 820.0 | 0.2334514658552196 | 1.0 | 0.5 | 0.5317145270071396 |
| 821.0 | 0.3750267027666355 | 1.0 | 0.5 | 0.9400927091701334 |
| 822.0 | 0.6114309357827568 | 1.0 | 0.5 | 1.8905687070019104 |
| 823.0 | 0.15551257370829796 | 1.0 | 0.5 | 0.338050863566619 |
| 824.0 | 0.23813670768097417 | 1.0 | 0.5 | 0.5439762915924459 |
| 825.0 | 0.870266707449991 | 1.0 | 0.5 | 4.084549063402345 |
| 826.0 | 3.154053223157938e-2 | 1.0 | 0.5 | 6.409729506779938e-2 |
| 827.0 | 0.1696763468222191 | 1.0 | 0.5 | 0.3718794212240955 |
| 828.0 | 0.9382537256649148 | 1.0 | 0.5 | 5.5694432798975 |
| 829.0 | 0.5690815690817298 | 1.0 | 0.5 | 1.6836729243990005 |
| 830.0 | 0.40173525398781396 | 1.0 | 0.5 | 1.027443807837522 |
| 831.0 | 0.37414920622824466 | 1.0 | 0.5 | 0.9372865697980035 |
| 832.0 | 0.8470235035676409 | 1.0 | 0.5 | 3.75494197495865 |
| 833.0 | 0.1639959478716596 | 1.0 | 0.5 | 0.35824363773142587 |
| 834.0 | 0.8417875076966232 | 1.0 | 0.5 | 3.6876325230218985 |
| 835.0 | 4.4800017902082656e-2 | 1.0 | 0.5 | 9.166911017102182e-2 |
| 836.0 | 8.371841878440967e-2 | 1.0 | 0.5 | 0.17486311694572496 |
| 837.0 | 0.792681678313694 | 1.0 | 0.5 | 3.1469997619491537 |
| 838.0 | 0.70931724616291 | 1.0 | 0.5 | 2.4710455990059654 |
| 839.0 | 0.9536753385934513 | 1.0 | 0.5 | 6.14416163184868 |
| 840.0 | 0.9602885160673758 | 1.0 | 0.5 | 6.452229730589149 |
| 841.0 | 0.7554874920826832 | 1.0 | 0.5 | 2.8169776284993024 |
| 842.0 | 0.13372214044518893 | 1.0 | 0.5 | 0.28709913578589247 |
| 843.0 | 0.5137668062151739 | 1.0 | 0.5 | 1.442133895114351 |
| 844.0 | 0.7646430480436962 | 1.0 | 0.5 | 2.893303945544842 |
| 845.0 | 0.737797471240314 | 1.0 | 0.5 | 2.677276126613299 |
| 846.0 | 0.2757355366840477 | 1.0 | 0.5 | 0.64519734494416 |
| 847.0 | 0.9070120314664122 | 1.0 | 0.5 | 4.750570329609125 |
| 848.0 | 0.8891902056378072 | 1.0 | 0.5 | 4.399880223610048 |
| 849.0 | 0.6345074545436008 | 1.0 | 0.5 | 2.0130187909368447 |
| 850.0 | 0.7278594124350969 | 1.0 | 0.5 | 2.602872960335896 |
| 851.0 | 0.7746614730325766 | 1.0 | 0.5 | 2.9803028864018035 |
| 852.0 | 0.1615769961852126 | 1.0 | 0.5 | 0.35246505607116085 |
| 853.0 | 0.791392456147658 | 1.0 | 0.5 | 3.134601145736619 |
| 854.0 | 0.6680598448264417 | 1.0 | 0.5 | 2.2056011636299084 |
| 855.0 | 8.13464244932427e-3 | 1.0 | 0.5 | 1.63358183694174e-2 |
| 856.0 | 0.10662505504031572 | 1.0 | 0.5 | 0.2254978301028323 |
| 857.0 | 0.9560295924470773 | 1.0 | 0.5 | 6.248476853892508 |
| 858.0 | 0.852060175832956 | 1.0 | 0.5 | 3.821899362730954 |
| 859.0 | 0.9719898411238275 | 1.0 | 0.5 | 7.150376035205891 |
| 860.0 | 0.2712547072167808 | 1.0 | 0.5 | 0.6328620012818229 |
| 861.0 | 0.5288209915498573 | 1.0 | 0.5 | 1.5050343935333608 |
| 862.0 | 0.7664883160339533 | 1.0 | 0.5 | 2.909046329453154 |
| 863.0 | 0.19497241124386178 | 1.0 | 0.5 | 0.433757460808162 |
| 864.0 | 0.22833492954177637 | 1.0 | 0.5 | 0.5184093393054244 |
| 865.0 | 0.8948424632463157 | 1.0 | 0.5 | 4.504591406380232 |
| 866.0 | 0.8605530887700942 | 1.0 | 0.5 | 3.9401426296279958 |
| 867.0 | 0.15650870586231802 | 1.0 | 0.5 | 0.34041139633305906 |
| 868.0 | 0.45930982890306726 | 1.0 | 0.5 | 1.2298177217350685 |
| 869.0 | 0.3246506528569505 | 1.0 | 0.5 | 0.7850503413401291 |
| 870.0 | 0.9510363593524436 | 1.0 | 0.5 | 6.033354567822288 |
| 871.0 | 0.5038715294705464 | 1.0 | 0.5 | 1.4018407452622141 |
| 872.0 | 5.1203794293428584e-2 | 1.0 | 0.5 | 0.10512249958235921 |
| 873.0 | 0.5544890265721415 | 1.0 | 0.5 | 1.617066801324528 |
| 874.0 | 0.2614676505710716 | 1.0 | 0.5 | 0.6061807474664205 |
| 875.0 | 5.243774821209923e-2 | 1.0 | 0.5 | 0.10772528617673659 |
| 876.0 | 0.7971971719215974 | 1.0 | 0.5 | 3.191042124405132 |
| 877.0 | 0.7919800318693259 | 1.0 | 0.5 | 3.140242406520687 |
| 878.0 | 0.5219283825848018 | 1.0 | 0.5 | 1.4759894609704223 |
| 879.0 | 0.4275949818719734 | 1.0 | 0.5 | 1.1158169290356357 |
| 880.0 | 0.6781991679783914 | 1.0 | 0.5 | 2.267644917806484 |
| 881.0 | 0.8932373960711312 | 1.0 | 0.5 | 4.4742951285035835 |
| 882.0 | 0.5478472624532599 | 1.0 | 0.5 | 1.5874704826077597 |
| 883.0 | 0.5685711063530478 | 1.0 | 0.5 | 1.6813051416100346 |
| 884.0 | 0.9721023498460244 | 1.0 | 0.5 | 7.158425635147378 |
| 885.0 | 0.21573425270663737 | 1.0 | 0.5 | 0.486014705364895 |
| 886.0 | 0.35259191127848954 | 1.0 | 0.5 | 0.8695568868089019 |
| 887.0 | 0.3472690789117947 | 1.0 | 0.5 | 0.853180600685918 |
| 888.0 | 0.5447448280041739 | 1.0 | 0.5 | 1.573794399314694 |
| 889.0 | 0.6727997991506037 | 1.0 | 0.5 | 2.2343661208216705 |
| 890.0 | 0.8696740488652007 | 1.0 | 0.5 | 4.0754333003570125 |
| 891.0 | 0.8734188023436674 | 1.0 | 0.5 | 4.133742596048804 |
| 892.0 | 0.29463161413393557 | 1.0 | 0.5 | 0.6980701589988371 |
| 893.0 | 3.283064353893961e-2 | 1.0 | 0.5 | 6.676332583206068e-2 |
| 894.0 | 0.23328215590600432 | 1.0 | 0.5 | 0.5312728295896356 |
| 895.0 | 0.40670843024467607 | 1.0 | 0.5 | 1.044138629788592 |
| 896.0 | 0.8227097978732368 | 1.0 | 0.5 | 3.45993465796266 |
| 897.0 | 0.7693837137837822 | 1.0 | 0.5 | 2.9340000964909345 |
| 898.0 | 0.5297116607776882 | 1.0 | 0.5 | 1.5088185693552791 |
| 899.0 | 0.7754688592512937 | 1.0 | 0.5 | 2.98748173968772 |
| 900.0 | 0.3248143864258809 | 1.0 | 0.5 | 0.7855352856755076 |
| 901.0 | 0.9401865839181958 | 1.0 | 0.5 | 5.633050587988034 |
| 902.0 | 0.34780122634541877 | 1.0 | 0.5 | 0.8548117918995641 |
| 903.0 | 0.7284097775344618 | 1.0 | 0.5 | 2.6069217674085974 |
| 904.0 | 0.9414922145837621 | 1.0 | 0.5 | 5.677190899121933 |
| 905.0 | 0.9905747699450492 | 1.0 | 0.5 | 9.328730273865226 |
| 906.0 | 0.6248601140341811 | 1.0 | 0.5 | 1.9609125866553052 |
| 907.0 | 2.9604572975377663e-2 | 1.0 | 0.5 | 6.010326765544391e-2 |
| 908.0 | 5.571585670694823e-3 | 1.0 | 0.5 | 1.1174329696467535e-2 |
| 909.0 | 0.6109088431526207 | 1.0 | 0.5 | 1.8878832528951692 |
| 910.0 | 0.4546914044998336 | 1.0 | 0.5 | 1.2128068285824642 |
| 911.0 | 6.989233588498944e-2 | 1.0 | 0.5 | 0.1449098633396298 |
| 912.0 | 7.423451728270214e-2 | 1.0 | 0.5 | 0.1542686696318581 |
| 913.0 | 0.5259803352971397 | 1.0 | 0.5 | 1.4930129428654308 |
| 914.0 | 0.658351708925572 | 1.0 | 0.5 | 2.1479469194904803 |
| 915.0 | 0.7900569250260353 | 1.0 | 0.5 | 3.121837713127503 |
| 916.0 | 0.23734310603381692 | 1.0 | 0.5 | 0.5418940581763712 |
| 917.0 | 0.5669448736548646 | 1.0 | 0.5 | 1.6737804930156697 |
| 918.0 | 0.32179695871984115 | 1.0 | 0.5 | 0.7766171299190336 |
| 919.0 | 0.10866609186678144 | 1.0 | 0.5 | 0.23007233022421117 |
| 920.0 | 0.45705634966886644 | 1.0 | 0.5 | 1.2214994782912616 |
| 921.0 | 0.9167978760940737 | 1.0 | 0.5 | 4.972964807530681 |
| 922.0 | 0.3082714338000597 | 1.0 | 0.5 | 0.7371232913772323 |
| 923.0 | 0.47735097863544884 | 1.0 | 0.5 | 1.2976902549082674 |
| 924.0 | 0.4404786851833078 | 1.0 | 0.5 | 1.161347311537362 |
| 925.0 | 0.7939848260637378 | 1.0 | 0.5 | 3.1596109060410877 |
| 926.0 | 0.2412573034838612 | 1.0 | 0.5 | 0.5521851246673014 |
| 927.0 | 0.3000287671367394 | 1.0 | 0.5 | 0.7134320813856403 |
| 928.0 | 0.6396708208326259 | 1.0 | 0.5 | 2.0414745575053637 |
| 929.0 | 0.7579547542491935 | 1.0 | 0.5 | 2.83726120877795 |
| 930.0 | 0.3396814387067095 | 1.0 | 0.5 | 0.8300657835650016 |
| 931.0 | 0.2892910263130618 | 1.0 | 0.5 | 0.6829845053640449 |
| 932.0 | 0.9011216837211008 | 1.0 | 0.5 | 4.6277306266829745 |
| 933.0 | 0.8210499505161656 | 1.0 | 0.5 | 3.441297130493794 |
| 934.0 | 0.20855830013501853 | 1.0 | 0.5 | 0.4677981203133048 |
| 935.0 | 0.20067661109578638 | 1.0 | 0.5 | 0.4479793460879907 |
| 936.0 | 0.2783975695108384 | 1.0 | 0.5 | 0.6525618840897621 |
| 937.0 | 8.228356433882655e-2 | 1.0 | 0.5 | 0.171733659388771 |
| 938.0 | 0.6509731323562694 | 1.0 | 0.5 | 2.105212750180706 |
| 939.0 | 0.22847862352050963 | 1.0 | 0.5 | 0.5187817997542151 |
| 940.0 | 0.8395983631124934 | 1.0 | 0.5 | 3.6601487571349267 |
| 941.0 | 0.9743748330931115 | 1.0 | 0.5 | 7.328360656210449 |
| 942.0 | 0.9784624250877514 | 1.0 | 0.5 | 7.675912397820349 |
| 943.0 | 0.30092332380443143 | 1.0 | 0.5 | 0.7159896972722992 |
| 944.0 | 0.9500911854862217 | 1.0 | 0.5 | 5.995115296523221 |
| 945.0 | 8.677317380760863e-2 | 1.0 | 0.5 | 0.1815419774519955 |
| 946.0 | 0.7801060191136249 | 1.0 | 0.5 | 3.0292195076905384 |
| 947.0 | 0.8407117298539127 | 1.0 | 0.5 | 3.674079397023548 |
| 948.0 | 2.5783170671583644e-2 | 1.0 | 0.5 | 5.2242765469739556e-2 |
| 949.0 | 0.6060746119884648 | 1.0 | 0.5 | 1.8631875162923288 |
| 950.0 | 0.5958910047385527 | 1.0 | 0.5 | 1.8121412943134505 |
| 951.0 | 0.1746502894487323 | 1.0 | 0.5 | 0.3838961817736315 |
| 952.0 | 0.5840255212924214 | 1.0 | 0.5 | 1.7542627397268897 |
| 953.0 | 0.39662332502701947 | 1.0 | 0.5 | 1.010427217997472 |
| 954.0 | 0.8294609650651269 | 1.0 | 0.5 | 3.5375821291253438 |
| 955.0 | 0.6431589573123884 | 1.0 | 0.5 | 2.060929709875304 |
| 956.0 | 0.985945005032187 | 1.0 | 0.5 | 8.52955486531813 |
| 957.0 | 0.6701686585706427 | 1.0 | 0.5 | 2.218347683497157 |
| 958.0 | 0.9097930895031728 | 1.0 | 0.5 | 4.8112984836026484 |
| 959.0 | 0.9461308232792471 | 1.0 | 0.5 | 5.842393650220309 |
| 960.0 | 5.2337673573452204e-2 | 1.0 | 0.5 | 0.10751407186495446 |
| 961.0 | 0.8460764659780871 | 1.0 | 0.5 | 3.7425986644134888 |
| 962.0 | 0.10514312394105463 | 1.0 | 0.5 | 0.22218297702597922 |
| 963.0 | 0.6975701033741791 | 1.0 | 0.5 | 2.3918115501203125 |
| 964.0 | 0.9576315406611462 | 1.0 | 0.5 | 6.322702154160914 |
| 965.0 | 0.5756726043081537 | 1.0 | 0.5 | 1.714499924122977 |
| 966.0 | 0.14180795869433915 | 1.0 | 0.5 | 0.3058547603470779 |
| 967.0 | 0.476602852426136 | 1.0 | 0.5 | 1.2948294774050015 |
| 968.0 | 0.5735933728030692 | 1.0 | 0.5 | 1.7047237280891907 |
| 969.0 | 0.21074148987963792 | 1.0 | 0.5 | 0.47332273812264397 |
| 970.0 | 0.4040283383810781 | 1.0 | 0.5 | 1.0351243213329395 |
| 971.0 | 0.44809616679520103 | 1.0 | 0.5 | 1.188762926181943 |
| 972.0 | 0.49753260138877997 | 1.0 | 0.5 | 1.376449039078463 |
| 973.0 | 0.3981895324367999 | 1.0 | 0.5 | 1.0156254423601343 |
| 974.0 | 0.4631341447354761 | 1.0 | 0.5 | 1.2440140393123216 |
| 975.0 | 0.7621220718869883 | 1.0 | 0.5 | 2.8719952879613677 |
| 976.0 | 0.35491286990322435 | 1.0 | 0.5 | 0.8767397717764418 |
| 977.0 | 0.5624416059435056 | 1.0 | 0.5 | 1.6530902199238997 |
| 978.0 | 0.9048958001682184 | 1.0 | 0.5 | 4.705564296273839 |
| 979.0 | 0.5871353426885598 | 1.0 | 0.5 | 1.769270891992473 |
| 980.0 | 0.9552974642377905 | 1.0 | 0.5 | 6.215450101021099 |
| 981.0 | 0.11683294268351829 | 1.0 | 0.5 | 0.24848180677799192 |
| 982.0 | 0.804763553103361 | 1.0 | 0.5 | 3.2670878135466657 |
| 983.0 | 0.9017746463190758 | 1.0 | 0.5 | 4.640981825644075 |
| 984.0 | 0.3546593618234335 | 1.0 | 0.5 | 0.8759539607786164 |
| 985.0 | 0.8175329055993971 | 1.0 | 0.5 | 3.4023708537413193 |
| 986.0 | 0.9271651336840998 | 1.0 | 0.5 | 5.239121011041082 |
| 987.0 | 0.43755514801054285 | 1.0 | 0.5 | 1.150924381235035 |
| 988.0 | 0.12335439229044076 | 1.0 | 0.5 | 0.2633049287820745 |
| 989.0 | 0.3823717678509384 | 1.0 | 0.5 | 0.9637371372795477 |
| 990.0 | 0.7134192888551714 | 1.0 | 0.5 | 2.499470136417535 |
| 991.0 | 0.5559806916407091 | 1.0 | 0.5 | 1.6237744603995141 |
| 992.0 | 0.7947950806945909 | 1.0 | 0.5 | 3.167492385499269 |
| 993.0 | 0.8593443260366468 | 1.0 | 0.5 | 3.9228808076956767 |
| 994.0 | 0.9695468897099642 | 1.0 | 0.5 | 6.983134291741286 |
| 995.0 | 9.645307355937993e-2 | 1.0 | 0.5 | 0.20285446358422873 |
| 996.0 | 0.5759218175673997 | 1.0 | 0.5 | 1.7156748964373492 |
| 997.0 | 0.8948271404741518 | 1.0 | 0.5 | 4.504300002523024 |
| 998.0 | 0.8288317043606008 | 1.0 | 0.5 | 3.5302160428926017 |
| 999.0 | 0.7727026977235016 | 1.0 | 0.5 | 2.962992833506503 |
dfExpRand.describe().show() // look sensible
+-------+-----------------+--------------------+----+----+--------------------+
|summary| Id| rand| one|rate| expo_sample|
+-------+-----------------+--------------------+----+----+--------------------+
| count| 1000| 1000|1000|1000| 1000|
| mean| 499.5| 0.5060789944940257| 1.0| 0.5| 2.024647367046763|
| stddev|288.8194360957494| 0.28795893414434537| 0.0| 0.0| 1.9649069025265538|
| min| 0|6.282087728304298E-4| 1.0| 0.5|0.001256812357281...|
| max| 999| 0.9981606891187609| 1.0| 0.5| 12.596728597154938|
+-------+-----------------+--------------------+----+----+--------------------+
val expoSamplesDF = spark.range(1000000000).toDF("Id") // just make a DF of 100 row indices
.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
.withColumn("one",lit(1.0))
.withColumn("rate",lit(0.5))
.withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand"))
expoSamplesDF: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
expoSamplesDF.describe().show()
+-------+--------------------+--------------------+----------+----------+--------------------+
|summary| Id| rand| one| rate| expo_sample|
+-------+--------------------+--------------------+----------+----------+--------------------+
| count| 1000000000| 1000000000|1000000000|1000000000| 1000000000|
| mean|4.9999999907595944E8| 0.49999521358240573| 1.0| 0.5| 1.9999814119383708|
| stddev|2.8867513473915565E8| 0.2886816216562552| 0.0| 0.0| 2.0000011916404206|
| min| 0|1.584746778249268...| 1.0| 0.5|3.169493556749679...|
| max| 999999999| 0.9999999996809935| 1.0| 0.5| 43.731619350261035|
+-------+--------------------+--------------------+----------+----------+--------------------+
Approximating Pi with Monte Carlo simulations
Uisng RDDs directly, let's estimate Pi.
//Calculate pi with Monte Carlo estimation
import scala.math.random
//make a very large unique set of 1 -> n
val partitions = 2
val n = math.min(100000L * partitions, Int.MaxValue).toInt
val xs = 1 until n
//split up n into the number of partitions we can use
val rdd = sc.parallelize(xs, partitions).setName("'N values rdd'")
//generate a random set of points within a 2x2 square
val sample = rdd.map { i =>
val x = random * 2 - 1
val y = random * 2 - 1
(x, y)
}.setName("'Random points rdd'")
//points w/in the square also w/in the center circle of r=1
val inside = sample.filter { case (x, y) => (x * x + y * y < 1) }.setName("'Random points inside circle'")
val count = inside.count()
//Area(circle)/Area(square) = inside/n => pi=4*inside/n
println("Pi is roughly " + 4.0 * count / n)
Pi is roughly 3.14156
import scala.math.random
partitions: Int = 2
n: Int = 200000
xs: scala.collection.immutable.Range = Range 1 until 200000
rdd: org.apache.spark.rdd.RDD[Int] = 'N values rdd' ParallelCollectionRDD[39] at parallelize at command-2971213210276568:10
sample: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points rdd' MapPartitionsRDD[40] at map at command-2971213210276568:13
inside: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points inside circle' MapPartitionsRDD[41] at filter at command-2971213210276568:20
count: Long = 157078
Doing it in PySpark is just as easy. This may be needed if there are pyhton libraries you want to take advantage of in Spark.
# # Estimating $\pi$
#
# This PySpark example shows you how to estimate $\pi$ in parallel
# using Monte Carlo integration.
from __future__ import print_function
import sys
from random import random
from operator import add
partitions = 2
n = 100000 * partitions
def f(_):
x = random() * 2 - 1
y = random() * 2 - 1
return 1 if x ** 2 + y ** 2 < 1 else 0
# To access the associated SparkContext
count = spark.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
print("Pi is roughly %f" % (4.0 * count / n))
Pi is roughly 3.138820
The following is from this google turotial.
Programming a Monte Carlo simulation in Scala
Monte Carlo, of course, is famous as a gambling destination. In this section, you use Scala to create a simulation that models the mathematical advantage that a casino enjoys in a game of chance. The "house edge" at a real casino varies widely from game to game; it can be over 20% in keno, for example. This tutorial creates a simple game where the house has only a one-percent advantage. Here's how the game works:
- The player places a bet, consisting of a number of chips from a bankroll fund.
- The player rolls a 100-sided die (how cool would that be?).
- If the result of the roll is a number from 1 to 49, the player wins.
- For results 50 to 100, the player loses the bet.
You can see that this game creates a one-percent disadvantage for the player: in 51 of the 100 possible outcomes for each roll, the player loses.
Follow these steps to create and run the game:
val STARTING_FUND = 10
val STAKE = 1 // the amount of the bet
val NUMBER_OF_GAMES = 25
def rollDie: Int = {
val r = scala.util.Random
r.nextInt(99) + 1
}
def playGame(stake: Int): (Int) = {
val faceValue = rollDie
if (faceValue < 50)
(2*stake)
else
(0)
}
// Function to play the game multiple times
// Returns the final fund amount
def playSession(
startingFund: Int = STARTING_FUND,
stake: Int = STAKE,
numberOfGames: Int = NUMBER_OF_GAMES):
(Int) = {
// Initialize values
var (currentFund, currentStake, currentGame) = (startingFund, 0, 1)
// Keep playing until number of games is reached or funds run out
while (currentGame <= numberOfGames && currentFund > 0) {
// Set the current bet and deduct it from the fund
currentStake = math.min(stake, currentFund)
currentFund -= currentStake
// Play the game
val (winnings) = playGame(currentStake)
// Add any winnings
currentFund += winnings
// Increment the loop counter
currentGame += 1
}
(currentFund)
}
STARTING_FUND: Int = 10
STAKE: Int = 1
NUMBER_OF_GAMES: Int = 25
rollDie: Int
playGame: (stake: Int)Int
playSession: (startingFund: Int, stake: Int, numberOfGames: Int)Int
Enter the following code to play the game 25 times, which is the default value for NUMBER_OF_GAMES.
playSession()
res31: Int = 5
Your bankroll started with a value of 10 units. Is it higher or lower, now?
Now simulate 10,000 players betting 100 chips per game. Play 10,000 games in a session. This Monte Carlo simulation calculates the probability of losing all your money before the end of the session. Enter the follow code:
(sc.parallelize(1 to 10000, 500)
.map(i => playSession(100000, 100, 250000))
.map(i => if (i == 0) 1 else 0)
.reduce(_+_)/10000.0)
res32: Double = 0.9992
Note that the syntax .reduce(_+_) is shorthand in Scala for aggregating by using a summing function.
The preceding code performs the following steps:
- Creates an RDD with the results of playing the session.
- Replaces bankrupt players' results with the number 1 and nonzero results with the number 0.
- Sums the count of bankrupt players.
- Divides the count by the number of players.
A typical result might be:
res32: Double = 0.9992
Which represents a near guarantee of losing all your money, even though the casino had only a one-percent advantage.
Project Ideas
Try to create a scalable simulation of interest to you.
Here are some mature projects:
- https://github.com/zishanfu/GeoSparkSim
- https://github.com/srbaird/mc-var-spark
See a more complete VaR modeling: - https://databricks.com/blog/2020/05/27/modernizing-risk-management-part-1-streaming-data-ingestion-rapid-model-development-and-monte-carlo-simulations-at-scale.html
Introduction to Machine Learning
Some very useful resources we will weave around for Statistical Learning, Data Mining, Machine Learning:
- January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR).
- https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
- free PDF of the ISLR book: http://www-bcf.usc.edu/~gareth/ISL/
- A more theoretically sound book with interesting aplications is Elements of Statistical Learning by the Stanford Gang of 3 (Hastie, Tibshirani and Friedman):
- free PDF of the 10th printing: http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
- Solutions: http://waxworksmath.com/Authors/GM/Hastie/WriteUp/weatherwaxepsteinhastiesolutions_manual.pdf
- A great series on Probabilistic ML by Kevin P. Murphy https://probml.github.io/pml-book/.
Deep Learning is a very popular method currently (2017) in Machine Learning and I recommend Andrew Ng's free course in Courseera for this if you have not taken a course in deep learning already.
Note: This is an applied course in data science and we will quickly move to doing things with data. You have to do work to get a deeper mathematical understanding or take other courses. We will focus on intution here and the distributed ML Pipeline in action.
I am assuming several of you have or are taking ML courses. In this course we will focus on getting our hads dirty quickly with some level of common understanding across all the disciplines represented here. Please dive deep at your own time to desired depths and tangents based on your background.
Summary of Machine Learning at a High Level
- A rough definition of machine learning.
- constructing and studying algorithms that learn from and make predictions on data.
- This broad area involves tools and ideas from various domains, including:
- computer science,
- probability and statistics,
- optimization,
- linear algebra
- logic
- etc.
- Common examples of ML, include:
- facial recognition,
- link prediction,
- text or document classification, eg.::
- spam detection,
- protein structure prediction
- teaching computers to play games (go!)
Some common terminology
using example of spam detection as a running example.
-
the data points we learn from are call observations:
- they are items or entities used for::
- learning or
- evaluation.
- they are items or entities used for::
-
in the context of spam detection,
- emails are our observations.
- Features are attributes used to represent an observation.
- Features are typically numeric,
- and in spam detection, they can be:
- the length,
- the date, or
- the presence or absence of keywords in emails.
- Labels are values or categories assigned to observations.
- and in spam detection, they can be:
- an email being defined as spam or not spam.
-
Training and test data sets are the observations that we use to train and evaluate a learning algorithm.
-
Pop-Quiz
- What is the difference between supervised and unsupervised learning?
For a Stats@Stanford Hastie-Tibshirani Perspective on Supervised and Unsupervised Learning:
(watch later 12:12):
From : https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/.
ML Pipelines
Here we will use ML Pipelines to do machine learning at scale.
See https://spark.apache.org/docs/latest/ml-pipeline.html for a quick overview.
Read this section for an overview:
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
When you first hear a song, do you ever categorize it as slow or fast in your head? Is it even a valid categorization? If so, can one do it automatically? I have always wondered about that. That is why I got excited when I learned about the Million Songs Dataset -Kaggle Challenge.
In this tutorial we will walk through a practical example of a data science project with Apache Spark in Python. We are going to parse, explore and model a sample from the million songs dataset stored on distributed storage. This tutorial is organized into three sections:
- ETL: Parses raw texts and creates a cached table
- Explore: Explores different aspects of the songs table using graphs
- Model: Uses SparkML to cluster songs based on some of their attributes

The goal of this tutorial is to prepare you for real world data science projects. Make sure you go through the tutorial in the above order and use the exercises to make yourself familiar further with the API. Also make sure you run these notebooks on a 1.6.x cluster.
1. ETL
The first step of most data science projects is extracting, transforming and loading data into well formated tables. Our example starts with ETL as well. By following the ETL noteboook you can expect to learn about following Spark concepts:
- RDD: Resilient Distributed Dataset
- Reading and transforming RDDs
- Schema in Spark
- Spark DataFrame
- Temp tables
- Caching tables
2. Explore
Exploratory analysis is a key step in any real data project. Data scientists use variety of tools to explore and visualize their data. In the second notebook of this tutorial we introduce several tools in Python and Databricks notebooks that can help you visually explore your large data. By reading this notebook and finishing its exercises you will become familiar with:
- How to view the schema of a table
- How to display ggplot and matplotlib figures in Notebooks
- How to summarize and visualize different aspects of large datasets
- How to sample and visualize large data
3. Model
The end goal of many data scientists is producing useful models. These models are often used for prediction of new and upcoming events in production. In our third notebook we construct a simple K-means clustering model. In this notebook you will learn about:
- Feature transformation
- Fitting a model using SparkML API
- Applying a model to data
- Visualizing model results
- Model tuning
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
CAUTION: This notebook is expected to have an error in command 28 (Cmd 28 in databricks notebook). You are meant to learn how to fix this error with simple exception-handling to become a better data scientist. So ignore this warning, if any.
Stage 1: Parsing songs data

This is the first notebook in this tutorial. In this notebook we will read data from DBFS (DataBricks FileSystem). We will parse data and load it as a table that can be readily used in following notebooks.
By going through this notebook you can expect to learn how to read distributed data as an RDD, how to transform RDDs, and how to construct a Spark DataFrame from an RDD and register it as a table.
We first explore different files in our distributed file system. We use a header file to construct a Spark Schema object. We write a function that takes the header and casts strings in each line of our data to corresponding types. Once we run this function on the data we find that it fails on some corner caes. We update our function and finally get a parsed RDD. We combine that RDD and the Schema to construct a DataFame and register it as a temporary table in SparkSQL.
Text data files are stored in dbfs:/databricks-datasets/songs/data-001
You can conveniently list files on distributed file system (DBFS, S3 or HDFS) using %fs commands.
ls /databricks-datasets/songs/data-001/
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/songs/data-001/header.txt | header.txt | 377.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00000 | part-00000 | 52837.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00001 | part-00001 | 52469.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00002 | part-00002 | 51778.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00003 | part-00003 | 50551.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00004 | part-00004 | 53449.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00005 | part-00005 | 53301.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00006 | part-00006 | 54184.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00007 | part-00007 | 50924.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00008 | part-00008 | 52533.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00009 | part-00009 | 54570.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00010 | part-00010 | 54338.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00011 | part-00011 | 51836.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00012 | part-00012 | 52297.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00013 | part-00013 | 52044.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00014 | part-00014 | 50704.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00015 | part-00015 | 54158.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00016 | part-00016 | 50080.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00017 | part-00017 | 47708.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00018 | part-00018 | 8858.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00019 | part-00019 | 53323.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00020 | part-00020 | 57877.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00021 | part-00021 | 52491.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00022 | part-00022 | 54791.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00023 | part-00023 | 50682.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00024 | part-00024 | 52863.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00025 | part-00025 | 47416.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00026 | part-00026 | 50130.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00027 | part-00027 | 53462.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00028 | part-00028 | 54179.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00029 | part-00029 | 52738.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00030 | part-00030 | 54159.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00031 | part-00031 | 51247.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00032 | part-00032 | 51610.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00033 | part-00033 | 53895.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00034 | part-00034 | 53125.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00035 | part-00035 | 54066.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00036 | part-00036 | 54265.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00037 | part-00037 | 54264.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00038 | part-00038 | 50540.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00039 | part-00039 | 55193.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00040 | part-00040 | 54537.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00041 | part-00041 | 52402.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00042 | part-00042 | 54673.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00043 | part-00043 | 53009.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00044 | part-00044 | 51789.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00045 | part-00045 | 52986.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00046 | part-00046 | 54442.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00047 | part-00047 | 52971.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00048 | part-00048 | 53331.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00049 | part-00049 | 44263.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00050 | part-00050 | 54841.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00051 | part-00051 | 54306.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00052 | part-00052 | 53610.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00053 | part-00053 | 53573.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00054 | part-00054 | 53854.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00055 | part-00055 | 54236.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00056 | part-00056 | 54455.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00057 | part-00057 | 52307.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00058 | part-00058 | 52313.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00059 | part-00059 | 52446.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00060 | part-00060 | 51958.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00061 | part-00061 | 53859.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00062 | part-00062 | 53698.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00063 | part-00063 | 54482.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00064 | part-00064 | 40182.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00065 | part-00065 | 54410.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00066 | part-00066 | 49123.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00067 | part-00067 | 50796.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00068 | part-00068 | 49561.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00069 | part-00069 | 52294.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00070 | part-00070 | 51250.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00071 | part-00071 | 58942.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00072 | part-00072 | 54589.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00073 | part-00073 | 54233.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00074 | part-00074 | 54725.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00075 | part-00075 | 54877.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00076 | part-00076 | 54333.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00077 | part-00077 | 51927.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00078 | part-00078 | 51744.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00079 | part-00079 | 53187.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00080 | part-00080 | 43246.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00081 | part-00081 | 54269.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00082 | part-00082 | 48464.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00083 | part-00083 | 52144.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00084 | part-00084 | 53375.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00085 | part-00085 | 55139.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00086 | part-00086 | 50924.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00087 | part-00087 | 52013.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00088 | part-00088 | 54262.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00089 | part-00089 | 53007.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00090 | part-00090 | 55142.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00091 | part-00091 | 52049.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00092 | part-00092 | 54714.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00093 | part-00093 | 52906.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00094 | part-00094 | 52188.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00095 | part-00095 | 50768.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00096 | part-00096 | 55242.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00097 | part-00097 | 52059.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00098 | part-00098 | 52982.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00099 | part-00099 | 52015.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00100 | part-00100 | 51467.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00101 | part-00101 | 50926.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00102 | part-00102 | 55018.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00103 | part-00103 | 50043.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00104 | part-00104 | 51936.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00105 | part-00105 | 57311.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00106 | part-00106 | 55090.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00107 | part-00107 | 54396.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00108 | part-00108 | 56594.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00109 | part-00109 | 53260.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00110 | part-00110 | 42007.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00119 | part-00119 | 0.0 |
As you can see in the listing we have data files and a single header file. The header file seems interesting and worth a first inspection at first. The file is 377 bytes, therefore it is safe to collect the entire content of the file in the notebook.
sc.textFile("databricks-datasets/songs/data-001/header.txt").collect()
res1: Array[String] = Array(artist_id:string, artist_latitude:double, artist_longitude:double, artist_location:string, artist_name:string, duration:double, end_of_fade_in:double, key:int, key_confidence:double, loudness:double, release:string, song_hotnes:double, song_id:string, start_of_fade_out:double, tempo:double, time_signature:double, time_signature_confidence:double, title:string, year:double, partial_sequence:int)
Remember you can collect() a huge RDD and crash the driver program - so it is a good practise to take a couple lines and count the number of lines, especially if you have no idea what file you are trying to read.
sc.textFile("databricks-datasets/songs/data-001/header.txt").take(2)
res2: Array[String] = Array(artist_id:string, artist_latitude:double)
sc.textFile("databricks-datasets/songs/data-001/header.txt").count()
res3: Long = 20
sc.textFile("databricks-datasets/songs/data-001/header.txt").collect.map(println) // uncomment to see line-by-line
artist_id:string
artist_latitude:double
artist_longitude:double
artist_location:string
artist_name:string
duration:double
end_of_fade_in:double
key:int
key_confidence:double
loudness:double
release:string
song_hotnes:double
song_id:string
start_of_fade_out:double
tempo:double
time_signature:double
time_signature_confidence:double
title:string
year:double
partial_sequence:int
res4: Array[Unit] = Array((), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ())
As seen above each line in the header consists of a name and a type separated by colon. We will need to parse the header file as follows:
val header = sc.textFile("/databricks-datasets/songs/data-001/header.txt").map(
line => {
val headerElement = line.split(":")
(headerElement(0), headerElement(1))
}
).collect()
header: Array[(String, String)] = Array((artist_id,string), (artist_latitude,double), (artist_longitude,double), (artist_location,string), (artist_name,string), (duration,double), (end_of_fade_in,double), (key,int), (key_confidence,double), (loudness,double), (release,string), (song_hotnes,double), (song_id,string), (start_of_fade_out,double), (tempo,double), (time_signature,double), (time_signature_confidence,double), (title,string), (year,double), (partial_sequence,int))
Let's define a case class called Song that will be used to represent each row of data in the files:
/databricks-datasets/songs/data-001/part-00000through/databricks-datasets/songs/data-001/part-00119or the last.../part-*****file.
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
defined class Song
Now we turn to data files. First, step is inspecting the first line of data to inspect its format.
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*")
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[54] at textFile at command-2971213210276292:2
dataRDD.count // number of songs
res5: Long = 31369
dataRDD.take(3)
res6: Array[String] = Array(AR81V6H1187FB48872 nan nan Earl Sixteen 213.7073 0.0 11 0.419 -12.106 Soldier of Jah Army nan SOVNZSZ12AB018A9B8 208.289 125.882 1 0.0 Rastaman 2003 --, ARVVZQP11E2835DBCB nan nan Wavves 133.25016 0.0 0 0.282 0.596 Wavvves 0.471578247701 SOJTQHQ12A8C143C5F 128.116 89.519 1 0.0 I Want To See You (And Go To The Movies) 2009 --, ARFG9M11187FB3BBCB nan nan Nashua USA C-Side 247.32689 0.0 9 0.612 -4.896 Santa Festival Compilation 2008 vol.1 nan SOAJSQL12AB0180501 242.196 171.278 5 1.0 Loose on the Dancefloor 0 225261)
Each line of data consists of multiple fields separated by \t. With that information and what we learned from the header file, we set out to parse our data.
- We have already created a case class based on the header (which seems to agree with the 3 lines above).
- Next, we will create a function that takes each line as input and returns the case class as output.
// let's do this 'by hand' to re-flex our RDD-muscles :)
// although this is not a robust way to read from a data engineering perspective (without fielding exceptions)
def parseLine(line: String): Song = {
val tokens = line.split("\t")
Song(tokens(0), tokens(1).toDouble, tokens(2).toDouble, tokens(3), tokens(4), tokens(5).toDouble, tokens(6).toDouble, tokens(7).toInt, tokens(8).toDouble, tokens(9).toDouble, tokens(10), tokens(11).toDouble, tokens(12), tokens(13).toDouble, tokens(14).toDouble, tokens(15).toDouble, tokens(16).toDouble, tokens(17), tokens(18).toDouble, tokens(19).toInt)
}
parseLine: (line: String)Song
With this function we can transform the dataRDD to another RDD that consists of Song case classes
val parsedRDD = dataRDD.map(parseLine)
parsedRDD: org.apache.spark.rdd.RDD[Song] = MapPartitionsRDD[55] at map at command-2971213210276298:1
To convert an RDD of case classes to a DataFrame, we just need to call the toDF method
val df = parsedRDD.toDF
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
Once we get a DataFrame we can register it as a temporary table. That will allow us to use its name in SQL queries.
df.createOrReplaceTempView("songsTable")
We can now cache our table. So far all operations have been lazy. This is the first time Spark will attempt to actually read all our data and apply the transformations.
If you are running Spark 1.6+ the next command will throw a parsing error.
cache table songsTable
The error means that we are trying to convert a missing value to a Double. Here is an updated version of the parseLine function to deal with missing values
// good data engineering science practise
def parseLine(line: String): Song = {
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
val tokens = line.split("\t")
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
parseLine: (line: String)Song
val df = dataRDD.map(parseLine).toDF
df.createOrReplaceTempView("songsTable")
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
And let's try caching the table. We are going to access this data multiple times in following notebooks, therefore it is a good idea to cache it in memory for faster subsequent access.
cache table songsTable
From now on we can easily query our data using the temporary table we just created and cached in memory. Since it is registered as a table we can conveniently use SQL as well as Spark API to access it.
select * from songsTable limit 10
| artist_id | artist_latitude | artist_longitude | artist_location | artist_name | duration | end_of_fade_in | key | key_confidence | loudness | release | song_hotness | song_id | start_of_fade_out | tempo | time_signature | time_signature_confidence | title | year | partial_sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AR81V6H1187FB48872 | 0.0 | 0.0 | Earl Sixteen | 213.7073 | 0.0 | 11.0 | 0.419 | -12.106 | Soldier of Jah Army | 0.0 | SOVNZSZ12AB018A9B8 | 208.289 | 125.882 | 1.0 | 0.0 | Rastaman | 2003.0 | -1.0 | |
| ARVVZQP11E2835DBCB | 0.0 | 0.0 | Wavves | 133.25016 | 0.0 | 0.0 | 0.282 | 0.596 | Wavvves | 0.471578247701 | SOJTQHQ12A8C143C5F | 128.116 | 89.519 | 1.0 | 0.0 | I Want To See You (And Go To The Movies) | 2009.0 | -1.0 | |
| ARFG9M11187FB3BBCB | 0.0 | 0.0 | Nashua USA | C-Side | 247.32689 | 0.0 | 9.0 | 0.612 | -4.896 | Santa Festival Compilation 2008 vol.1 | 0.0 | SOAJSQL12AB0180501 | 242.196 | 171.278 | 5.0 | 1.0 | Loose on the Dancefloor | 0.0 | 225261.0 |
| ARK4Z2O1187FB45FF0 | 0.0 | 0.0 | Harvest | 337.05751 | 0.247 | 4.0 | 0.46 | -9.092 | Underground Community | 0.0 | SOTDRVW12AB018BEB9 | 327.436 | 84.986 | 4.0 | 0.673 | No Return | 0.0 | 101619.0 | |
| AR4VQSG1187FB57E18 | 35.25082 | -91.74015 | Searcy, AR | Gossip | 430.23628 | 0.0 | 2.0 | 3.4e-2 | -6.846 | Yr Mangled Heart | 0.0 | SOTVOCL12A8AE478DD | 424.06 | 121.998 | 4.0 | 0.847 | Yr Mangled Heart | 2006.0 | 740623.0 |
| ARNBV1X1187B996249 | 0.0 | 0.0 | Alex | 186.80118 | 0.0 | 4.0 | 0.641 | -16.108 | Jolgaledin | 0.0 | SODTGRY12AB0182438 | 166.156 | 140.735 | 4.0 | 5.5e-2 | Mariu Sonur Jesus | 0.0 | 673970.0 | |
| ARXOEZX1187B9B82A1 | 0.0 | 0.0 | Elie Attieh | 361.89995 | 0.0 | 7.0 | 0.863 | -4.919 | ELITE | 0.0 | SOIINTJ12AB0180BA6 | 354.476 | 128.024 | 4.0 | 0.399 | Fe Yom We Leila | 0.0 | 280304.0 | |
| ARXPUIA1187B9A32F1 | 0.0 | 0.0 | Rome, Italy | Simone Cristicchi | 220.00281 | 2.119 | 4.0 | 0.486 | -6.52 | Dall'Altra Parte Del Cancello | 0.484225272411 | SONHXJK12AAF3B5290 | 214.761 | 99.954 | 1.0 | 0.928 | L'Italiano | 2007.0 | 745962.0 |
| ARNPPTH1187B9AD429 | 51.4855 | -0.37196 | Heston, Middlesex, England | Jimmy Page | 156.86485 | 0.334 | 7.0 | 0.493 | -9.962 | No Introduction Necessary [Deluxe Edition] | 0.0 | SOGUHGW12A58A80E06 | 149.269 | 162.48 | 4.0 | 0.534 | Wailing Sounds | 2004.0 | 599250.0 |
| AROGWRA122988FEE45 | 0.0 | 0.0 | Christos Dantis | 256.67873 | 2.537 | 9.0 | 0.742 | -13.404 | Daktilika Apotipomata | 0.0 | SOJJOYI12A8C13399D | 248.912 | 134.944 | 4.0 | 0.162 | Stin Proigoumeni Zoi | 0.0 | 611396.0 |
Next up is exploring this data. Click on the Exploration notebook to continue the tutorial.
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
Stage 2: Exploring songs data

This is the second notebook in this tutorial. In this notebook we do what any data scientist does with their data right after parsing it: exploring and understanding different aspect of data. Make sure you understand how we get the songsTable by reading and running the ETL notebook. In the ETL notebook we created and cached a temporary table named songsTable
// Let's quickly do everything to register the tempView of the table here
// fill in comment ... EXERCISE!
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
def parseLine(line: String): Song = {
// fill in comment ...
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
// fill in comment ...
val tokens = line.split("\t")
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*")
// .. fill in comment
val df = dataRDD.map(parseLine).toDF
// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[81] at textFile at command-2971213210276755:30
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
spark.catalog.listTables.show(false) // make sure the temp view of our table is there
+----------------------------+--------+-----------+---------+-----------+
|name |database|description|tableType|isTemporary|
+----------------------------+--------+-----------+---------+-----------+
|countrycodes |default |null |EXTERNAL |false |
|ltcar_locations_2_csv |default |null |MANAGED |false |
|magellan |default |null |MANAGED |false |
|mobile_sample |default |null |EXTERNAL |false |
|over300all_2_txt |default |null |MANAGED |false |
|person |default |null |MANAGED |false |
|persons |default |null |MANAGED |false |
|social_media_usage |default |null |MANAGED |false |
|social_media_usage_csv_gui |default |null |MANAGED |false |
|voronoi20191213uppsla1st_txt|default |null |MANAGED |false |
|voronoi20191213uppsla2d_txt |default |null |MANAGED |false |
|voronoi20191213uppsla3d_txt |default |null |MANAGED |false |
|songstable |null |null |TEMPORARY|true |
+----------------------------+--------+-----------+---------+-----------+
A first inspection
A first step to any data exploration is viewing sample data. For this purpose we can use a simple SQL query that returns first 10 rows.
select * from songsTable limit 10
| artist_id | artist_latitude | artist_longitude | artist_location | artist_name | duration | end_of_fade_in | key | key_confidence | loudness | release | song_hotness | song_id | start_of_fade_out | tempo | time_signature | time_signature_confidence | title | year | partial_sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AR81V6H1187FB48872 | 0.0 | 0.0 | Earl Sixteen | 213.7073 | 0.0 | 11.0 | 0.419 | -12.106 | Soldier of Jah Army | 0.0 | SOVNZSZ12AB018A9B8 | 208.289 | 125.882 | 1.0 | 0.0 | Rastaman | 2003.0 | -1.0 | |
| ARVVZQP11E2835DBCB | 0.0 | 0.0 | Wavves | 133.25016 | 0.0 | 0.0 | 0.282 | 0.596 | Wavvves | 0.471578247701 | SOJTQHQ12A8C143C5F | 128.116 | 89.519 | 1.0 | 0.0 | I Want To See You (And Go To The Movies) | 2009.0 | -1.0 | |
| ARFG9M11187FB3BBCB | 0.0 | 0.0 | Nashua USA | C-Side | 247.32689 | 0.0 | 9.0 | 0.612 | -4.896 | Santa Festival Compilation 2008 vol.1 | 0.0 | SOAJSQL12AB0180501 | 242.196 | 171.278 | 5.0 | 1.0 | Loose on the Dancefloor | 0.0 | 225261.0 |
| ARK4Z2O1187FB45FF0 | 0.0 | 0.0 | Harvest | 337.05751 | 0.247 | 4.0 | 0.46 | -9.092 | Underground Community | 0.0 | SOTDRVW12AB018BEB9 | 327.436 | 84.986 | 4.0 | 0.673 | No Return | 0.0 | 101619.0 | |
| AR4VQSG1187FB57E18 | 35.25082 | -91.74015 | Searcy, AR | Gossip | 430.23628 | 0.0 | 2.0 | 3.4e-2 | -6.846 | Yr Mangled Heart | 0.0 | SOTVOCL12A8AE478DD | 424.06 | 121.998 | 4.0 | 0.847 | Yr Mangled Heart | 2006.0 | 740623.0 |
| ARNBV1X1187B996249 | 0.0 | 0.0 | Alex | 186.80118 | 0.0 | 4.0 | 0.641 | -16.108 | Jolgaledin | 0.0 | SODTGRY12AB0182438 | 166.156 | 140.735 | 4.0 | 5.5e-2 | Mariu Sonur Jesus | 0.0 | 673970.0 | |
| ARXOEZX1187B9B82A1 | 0.0 | 0.0 | Elie Attieh | 361.89995 | 0.0 | 7.0 | 0.863 | -4.919 | ELITE | 0.0 | SOIINTJ12AB0180BA6 | 354.476 | 128.024 | 4.0 | 0.399 | Fe Yom We Leila | 0.0 | 280304.0 | |
| ARXPUIA1187B9A32F1 | 0.0 | 0.0 | Rome, Italy | Simone Cristicchi | 220.00281 | 2.119 | 4.0 | 0.486 | -6.52 | Dall'Altra Parte Del Cancello | 0.484225272411 | SONHXJK12AAF3B5290 | 214.761 | 99.954 | 1.0 | 0.928 | L'Italiano | 2007.0 | 745962.0 |
| ARNPPTH1187B9AD429 | 51.4855 | -0.37196 | Heston, Middlesex, England | Jimmy Page | 156.86485 | 0.334 | 7.0 | 0.493 | -9.962 | No Introduction Necessary [Deluxe Edition] | 0.0 | SOGUHGW12A58A80E06 | 149.269 | 162.48 | 4.0 | 0.534 | Wailing Sounds | 2004.0 | 599250.0 |
| AROGWRA122988FEE45 | 0.0 | 0.0 | Christos Dantis | 256.67873 | 2.537 | 9.0 | 0.742 | -13.404 | Daktilika Apotipomata | 0.0 | SOJJOYI12A8C13399D | 248.912 | 134.944 | 4.0 | 0.162 | Stin Proigoumeni Zoi | 0.0 | 611396.0 |
table("songsTable").printSchema()
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
select count(*) from songsTable
| count(1) |
|---|
| 31369.0 |
table("songsTable").count() // or equivalently with DataFrame API - recall table("songsTable") is a DataFrame
res4: Long = 31369
display(sqlContext.sql("SELECT duration, year FROM songsTable")) // Aggregation is set to 'Average' in 'Plot Options'
| duration | year |
|---|---|
| 213.7073 | 2003.0 |
| 133.25016 | 2009.0 |
| 247.32689 | 0.0 |
| 337.05751 | 0.0 |
| 430.23628 | 2006.0 |
| 186.80118 | 0.0 |
| 361.89995 | 0.0 |
| 220.00281 | 2007.0 |
| 156.86485 | 2004.0 |
| 256.67873 | 0.0 |
| 204.64281 | 0.0 |
| 112.48281 | 0.0 |
| 170.39628 | 0.0 |
| 215.95383 | 0.0 |
| 303.62077 | 0.0 |
| 266.60526 | 0.0 |
| 326.19057 | 1997.0 |
| 51.04281 | 2009.0 |
| 129.4624 | 0.0 |
| 253.33506 | 2003.0 |
| 237.76608 | 2004.0 |
| 132.96281 | 0.0 |
| 399.35955 | 2006.0 |
| 168.75057 | 1991.0 |
| 396.042 | 0.0 |
| 192.10404 | 1968.0 |
| 12.2771 | 2006.0 |
| 367.56853 | 0.0 |
| 189.93587 | 0.0 |
| 233.50812 | 0.0 |
| 462.68036 | 0.0 |
| 202.60526 | 0.0 |
| 241.52771 | 0.0 |
| 275.64363 | 1992.0 |
| 350.69342 | 2007.0 |
| 166.55628 | 1968.0 |
| 249.49506 | 1983.0 |
| 53.86404 | 1992.0 |
| 233.76934 | 2001.0 |
| 275.12118 | 2009.0 |
| 191.13751 | 2006.0 |
| 299.07546 | 0.0 |
| 468.74077 | 0.0 |
| 110.34077 | 0.0 |
| 234.78812 | 2003.0 |
| 705.25342 | 2006.0 |
| 383.52934 | 0.0 |
| 196.10077 | 0.0 |
| 299.20608 | 1998.0 |
| 94.04036 | 0.0 |
| 28.08118 | 2006.0 |
| 207.93424 | 2006.0 |
| 152.0322 | 1999.0 |
| 207.96036 | 2002.0 |
| 371.25179 | 0.0 |
| 288.93995 | 2002.0 |
| 235.93751 | 2004.0 |
| 505.70404 | 0.0 |
| 177.57995 | 0.0 |
| 376.842 | 2004.0 |
| 266.84036 | 2004.0 |
| 270.8371 | 2006.0 |
| 178.18077 | 0.0 |
| 527.17669 | 0.0 |
| 244.27057 | 0.0 |
| 436.47955 | 2006.0 |
| 236.79955 | 0.0 |
| 134.53016 | 2005.0 |
| 181.002 | 0.0 |
| 239.41179 | 1999.0 |
| 72.98567 | 0.0 |
| 214.36036 | 2001.0 |
| 150.59546 | 2007.0 |
| 152.45016 | 1970.0 |
| 218.17424 | 0.0 |
| 290.63791 | 0.0 |
| 149.05424 | 0.0 |
| 440.21506 | 0.0 |
| 212.34893 | 1988.0 |
| 278.67383 | 0.0 |
| 269.60934 | 1974.0 |
| 182.69995 | 2002.0 |
| 207.882 | 2007.0 |
| 102.50404 | 0.0 |
| 437.60281 | 0.0 |
| 216.11057 | 2009.0 |
| 193.25342 | 0.0 |
| 234.16118 | 2009.0 |
| 695.77098 | 0.0 |
| 297.58649 | 1996.0 |
| 265.37751 | 2000.0 |
| 182.85669 | 1990.0 |
| 202.23955 | 0.0 |
| 390.08608 | 2009.0 |
| 242.78159 | 2000.0 |
| 242.54649 | 2002.0 |
| 496.66567 | 2004.0 |
| 395.36281 | 0.0 |
| 234.89261 | 1999.0 |
| 237.84444 | 2005.0 |
| 313.57342 | 2009.0 |
| 489.22077 | 2001.0 |
| 239.98649 | 2004.0 |
| 128.65261 | 0.0 |
| 193.07057 | 0.0 |
| 144.19546 | 0.0 |
| 196.96281 | 2006.0 |
| 222.06649 | 1997.0 |
| 58.38322 | 0.0 |
| 346.14812 | 1998.0 |
| 406.54322 | 0.0 |
| 304.09098 | 2009.0 |
| 180.21832 | 2003.0 |
| 213.41995 | 0.0 |
| 323.44771 | 0.0 |
| 54.7522 | 2009.0 |
| 437.02812 | 1994.0 |
| 268.7473 | 2009.0 |
| 104.75057 | 0.0 |
| 248.60689 | 2006.0 |
| 221.41342 | 0.0 |
| 237.81832 | 1991.0 |
| 216.34567 | 2009.0 |
| 78.94159 | 0.0 |
| 47.22893 | 2005.0 |
| 202.00444 | 2007.0 |
| 293.56363 | 0.0 |
| 206.44526 | 1986.0 |
| 267.78077 | 2003.0 |
| 187.27138 | 2008.0 |
| 249.05098 | 2009.0 |
| 221.51791 | 0.0 |
| 452.88444 | 0.0 |
| 163.76118 | 1992.0 |
| 257.17506 | 0.0 |
| 235.78077 | 0.0 |
| 257.82812 | 1996.0 |
| 195.34322 | 0.0 |
| 478.1971 | 0.0 |
| 268.01587 | 1997.0 |
| 136.93342 | 1983.0 |
| 397.53098 | 0.0 |
| 194.69016 | 2001.0 |
| 580.80608 | 0.0 |
| 177.71057 | 2006.0 |
| 257.43628 | 1999.0 |
| 184.13669 | 0.0 |
| 64.57424 | 2001.0 |
| 123.92444 | 1993.0 |
| 257.07057 | 0.0 |
| 219.48036 | 1996.0 |
| 679.41832 | 0.0 |
| 252.29016 | 1995.0 |
| 311.90159 | 2004.0 |
| 252.76036 | 1998.0 |
| 138.94485 | 0.0 |
| 428.64281 | 0.0 |
| 295.31383 | 0.0 |
| 212.03546 | 0.0 |
| 426.50077 | 0.0 |
| 197.11955 | 0.0 |
| 191.55546 | 0.0 |
| 187.53261 | 2006.0 |
| 184.97261 | 2004.0 |
| 388.41424 | 2009.0 |
| 218.90567 | 0.0 |
| 246.49098 | 0.0 |
| 452.88444 | 0.0 |
| 223.18975 | 0.0 |
| 245.2371 | 0.0 |
| 148.92363 | 0.0 |
| 362.81424 | 2005.0 |
| 171.44118 | 0.0 |
| 207.72526 | 2005.0 |
| 191.29424 | 0.0 |
| 208.50893 | 0.0 |
| 240.24771 | 1995.0 |
| 373.44608 | 2002.0 |
| 172.01587 | 0.0 |
| 153.25995 | 2007.0 |
| 242.36363 | 1994.0 |
| 177.55383 | 0.0 |
| 263.20934 | 1994.0 |
| 191.03302 | 2007.0 |
| 232.77669 | 0.0 |
| 220.65587 | 0.0 |
| 132.57098 | 2002.0 |
| 189.6224 | 1993.0 |
| 32.522 | 1997.0 |
| 173.94893 | 0.0 |
| 268.01587 | 2006.0 |
| 91.97669 | 0.0 |
| 215.77098 | 0.0 |
| 195.47383 | 0.0 |
| 234.81424 | 1977.0 |
| 110.78485 | 0.0 |
| 155.74159 | 0.0 |
| 172.5122 | 0.0 |
| 227.76118 | 1995.0 |
| 233.01179 | 2007.0 |
| 298.89261 | 0.0 |
| 245.36771 | 1994.0 |
| 276.08771 | 2005.0 |
| 375.77098 | 2003.0 |
| 273.71057 | 0.0 |
| 226.92526 | 0.0 |
| 196.46649 | 0.0 |
| 199.65342 | 1995.0 |
| 243.40853 | 0.0 |
| 207.62077 | 2006.0 |
| 252.73424 | 0.0 |
| 244.32281 | 0.0 |
| 152.65914 | 0.0 |
| 203.88526 | 2003.0 |
| 120.16281 | 0.0 |
| 214.77832 | 1977.0 |
| 204.9824 | 0.0 |
| 118.30812 | 1996.0 |
| 205.26975 | 0.0 |
| 499.22567 | 0.0 |
| 217.83465 | 2005.0 |
| 192.57424 | 2005.0 |
| 328.09751 | 0.0 |
| 298.03057 | 1968.0 |
| 501.49832 | 0.0 |
| 276.40118 | 0.0 |
| 507.55873 | 2006.0 |
| 191.08526 | 2008.0 |
| 324.38812 | 0.0 |
| 218.56608 | 0.0 |
| 232.30649 | 0.0 |
| 295.05261 | 1972.0 |
| 225.74975 | 2003.0 |
| 522.00444 | 0.0 |
| 245.86404 | 1967.0 |
| 263.67955 | 0.0 |
| 556.61669 | 2009.0 |
| 227.94404 | 1998.0 |
| 83.82649 | 1964.0 |
| 242.85995 | 0.0 |
| 233.09016 | 2008.0 |
| 201.74322 | 0.0 |
| 476.15955 | 0.0 |
| 370.93832 | 2005.0 |
| 229.17179 | 0.0 |
| 288.07791 | 2001.0 |
| 91.34975 | 0.0 |
| 230.79138 | 2005.0 |
| 256.46975 | 2003.0 |
| 203.44118 | 0.0 |
| 230.81751 | 2003.0 |
| 272.29995 | 0.0 |
| 201.22077 | 2008.0 |
| 204.93016 | 2010.0 |
| 372.84526 | 0.0 |
| 63.65995 | 2005.0 |
| 412.15955 | 0.0 |
| 270.10567 | 0.0 |
| 104.6722 | 0.0 |
| 214.25587 | 1970.0 |
| 230.05995 | 0.0 |
| 155.74159 | 0.0 |
| 218.04363 | 2008.0 |
| 357.77261 | 2007.0 |
| 318.27546 | 1985.0 |
| 444.55138 | 2010.0 |
| 509.07383 | 0.0 |
| 176.95302 | 0.0 |
| 95.34649 | 0.0 |
| 207.67302 | 0.0 |
| 256.67873 | 1994.0 |
| 252.78649 | 0.0 |
| 234.60526 | 0.0 |
| 167.65342 | 0.0 |
| 266.16118 | 0.0 |
| 188.05506 | 0.0 |
| 229.14567 | 2009.0 |
| 227.00363 | 2004.0 |
| 74.50077 | 1992.0 |
| 222.09261 | 0.0 |
| 212.68853 | 1984.0 |
| 155.74159 | 0.0 |
| 153.65179 | 0.0 |
| 548.51873 | 0.0 |
| 445.90975 | 2003.0 |
| 317.49179 | 1999.0 |
| 140.32934 | 0.0 |
| 309.4722 | 0.0 |
| 142.91546 | 0.0 |
| 429.24363 | 2007.0 |
| 172.19873 | 0.0 |
| 215.562 | 0.0 |
| 290.79465 | 2009.0 |
| 197.04118 | 0.0 |
| 309.44608 | 0.0 |
| 265.01179 | 1999.0 |
| 257.64526 | 2000.0 |
| 203.54567 | 0.0 |
| 161.56689 | 0.0 |
| 177.84118 | 0.0 |
| 260.04853 | 2004.0 |
| 195.00363 | 1988.0 |
| 268.042 | 0.0 |
| 195.97016 | 1991.0 |
| 351.92118 | 0.0 |
| 119.35302 | 0.0 |
| 177.24036 | 0.0 |
| 259.83955 | 0.0 |
| 222.51057 | 2008.0 |
| 163.97016 | 2004.0 |
| 139.49342 | 0.0 |
| 158.77179 | 0.0 |
| 193.4624 | 2000.0 |
| 131.082 | 1963.0 |
| 190.95465 | 1998.0 |
| 413.3873 | 2005.0 |
| 134.73914 | 1966.0 |
| 162.40281 | 1965.0 |
| 243.59138 | 1965.0 |
| 180.84526 | 0.0 |
| 315.14077 | 0.0 |
| 221.51791 | 1994.0 |
| 122.53995 | 2008.0 |
| 243.43465 | 1990.0 |
| 200.202 | 1982.0 |
| 95.50322 | 2000.0 |
| 200.4371 | 1998.0 |
| 186.93179 | 0.0 |
| 492.22485 | 1999.0 |
| 359.33995 | 1972.0 |
| 89.39057 | 1990.0 |
| 212.81914 | 0.0 |
| 315.03628 | 1996.0 |
| 214.69995 | 0.0 |
| 137.92608 | 1993.0 |
| 559.49016 | 0.0 |
| 382.14485 | 1991.0 |
| 430.31465 | 2008.0 |
| 171.25832 | 0.0 |
| 210.12853 | 2002.0 |
| 53.18485 | 2005.0 |
| 78.65424 | 1993.0 |
| 209.162 | 2008.0 |
| 237.60934 | 2006.0 |
| 184.47628 | 2009.0 |
| 323.02975 | 1997.0 |
| 158.27546 | 0.0 |
| 213.86404 | 0.0 |
| 470.69995 | 0.0 |
| 229.79873 | 2005.0 |
| 392.22812 | 0.0 |
| 196.62322 | 0.0 |
| 80.97914 | 0.0 |
| 124.55138 | 1989.0 |
| 230.32118 | 1971.0 |
| 132.51873 | 0.0 |
| 112.95302 | 1994.0 |
| 131.52608 | 0.0 |
| 153.25995 | 2010.0 |
| 211.01669 | 0.0 |
| 218.93179 | 2008.0 |
| 175.0722 | 2010.0 |
| 116.61016 | 1997.0 |
| 251.45424 | 2001.0 |
| 269.50485 | 2004.0 |
| 231.47057 | 0.0 |
| 298.37016 | 1996.0 |
| 314.122 | 2005.0 |
| 263.99302 | 0.0 |
| 480.91383 | 2001.0 |
| 305.10975 | 0.0 |
| 280.16281 | 0.0 |
| 295.65342 | 1999.0 |
| 411.45424 | 2007.0 |
| 265.97832 | 0.0 |
| 153.96526 | 0.0 |
| 210.31138 | 1970.0 |
| 241.44934 | 0.0 |
| 235.33669 | 0.0 |
| 352.65261 | 0.0 |
| 293.35465 | 0.0 |
| 243.66975 | 2003.0 |
| 133.22404 | 0.0 |
| 233.03791 | 0.0 |
| 339.93098 | 0.0 |
| 249.80853 | 1993.0 |
| 253.72689 | 2004.0 |
| 94.35383 | 1981.0 |
| 130.63791 | 0.0 |
| 195.36934 | 0.0 |
| 229.25016 | 2007.0 |
| 314.64444 | 2007.0 |
| 329.1424 | 1998.0 |
| 224.46975 | 1990.0 |
| 215.562 | 1987.0 |
| 236.85179 | 1990.0 |
| 197.11955 | 1957.0 |
| 251.76771 | 2004.0 |
| 183.50975 | 0.0 |
| 268.01587 | 2005.0 |
| 413.02159 | 0.0 |
| 385.17506 | 2000.0 |
| 358.16444 | 0.0 |
| 164.77995 | 0.0 |
| 253.36118 | 2004.0 |
| 196.49261 | 2007.0 |
| 157.6224 | 1999.0 |
| 310.93506 | 0.0 |
| 434.96444 | 1991.0 |
| 157.04771 | 1991.0 |
| 266.16118 | 2007.0 |
| 267.59791 | 1977.0 |
| 303.90812 | 0.0 |
| 277.18485 | 2009.0 |
| 272.22159 | 0.0 |
| 155.95057 | 0.0 |
| 127.00689 | 1997.0 |
| 152.86812 | 2005.0 |
| 224.7571 | 1990.0 |
| 175.41179 | 0.0 |
| 151.97995 | 0.0 |
| 199.99302 | 0.0 |
| 251.53261 | 0.0 |
| 252.96934 | 2004.0 |
| 181.13261 | 1984.0 |
| 195.49995 | 0.0 |
| 328.202 | 2001.0 |
| 187.71546 | 0.0 |
| 166.94812 | 1985.0 |
| 242.72934 | 1988.0 |
| 218.80118 | 2005.0 |
| 205.68771 | 0.0 |
| 146.93832 | 1996.0 |
| 449.4624 | 2000.0 |
| 503.40526 | 0.0 |
| 181.34159 | 0.0 |
| 143.90812 | 0.0 |
| 406.36036 | 0.0 |
| 269.87057 | 0.0 |
| 265.29914 | 0.0 |
| 242.88608 | 0.0 |
| 110.39302 | 0.0 |
| 262.84363 | 0.0 |
| 334.00118 | 1990.0 |
| 173.81832 | 2007.0 |
| 608.78322 | 0.0 |
| 197.22404 | 0.0 |
| 163.94404 | 2008.0 |
| 93.09995 | 2001.0 |
| 206.75873 | 0.0 |
| 183.50975 | 0.0 |
| 402.442 | 0.0 |
| 735.79057 | 1986.0 |
| 233.19465 | 1997.0 |
| 326.55628 | 0.0 |
| 525.50485 | 0.0 |
| 396.19873 | 0.0 |
| 171.12771 | 0.0 |
| 318.1971 | 2006.0 |
| 323.70893 | 2002.0 |
| 526.99383 | 0.0 |
| 161.09669 | 1991.0 |
| 168.41098 | 1990.0 |
| 249.57342 | 0.0 |
| 405.4722 | 0.0 |
| 271.0722 | 2010.0 |
| 190.69342 | 2009.0 |
| 151.61424 | 2001.0 |
| 121.57342 | 0.0 |
| 117.08036 | 0.0 |
| 244.24444 | 2008.0 |
| 246.85669 | 0.0 |
| 144.03873 | 2007.0 |
| 169.79546 | 1988.0 |
| 193.93261 | 2004.0 |
| 325.77261 | 0.0 |
| 337.34485 | 0.0 |
| 143.67302 | 2009.0 |
| 211.69587 | 0.0 |
| 299.4673 | 1978.0 |
| 159.76444 | 0.0 |
| 337.31873 | 0.0 |
| 259.18649 | 2007.0 |
| 221.64853 | 0.0 |
| 164.54485 | 0.0 |
| 56.34567 | 0.0 |
| 184.21506 | 0.0 |
| 249.23383 | 2010.0 |
| 127.29424 | 1994.0 |
| 306.6771 | 1980.0 |
| 168.98567 | 0.0 |
| 290.2722 | 0.0 |
| 182.33424 | 2004.0 |
| 180.92363 | 0.0 |
| 233.76934 | 1990.0 |
| 423.70567 | 0.0 |
| 139.36281 | 0.0 |
| 289.72363 | 2005.0 |
| 100.96281 | 2005.0 |
| 153.05098 | 2009.0 |
| 129.25342 | 0.0 |
| 190.11873 | 1993.0 |
| 158.1971 | 0.0 |
| 234.94485 | 2000.0 |
| 256.02567 | 0.0 |
| 279.84934 | 0.0 |
| 217.7824 | 2005.0 |
| 271.62077 | 2005.0 |
| 372.34893 | 0.0 |
| 264.88118 | 0.0 |
| 270.18404 | 1984.0 |
| 42.86649 | 0.0 |
| 247.27465 | 0.0 |
| 185.10322 | 1990.0 |
| 333.94893 | 0.0 |
| 380.49914 | 1999.0 |
| 517.72036 | 0.0 |
| 208.95302 | 2006.0 |
| 359.73179 | 0.0 |
| 378.72281 | 1995.0 |
| 110.41914 | 0.0 |
| 237.37424 | 2003.0 |
| 136.30649 | 0.0 |
| 153.73016 | 2005.0 |
| 209.8673 | 2007.0 |
| 224.86159 | 0.0 |
| 202.34404 | 0.0 |
| 229.43302 | 0.0 |
| 300.56444 | 2003.0 |
| 264.35873 | 0.0 |
| 213.9424 | 0.0 |
| 164.77995 | 2004.0 |
| 206.75873 | 0.0 |
| 249.73016 | 2009.0 |
| 521.11628 | 2002.0 |
| 240.09098 | 0.0 |
| 347.89832 | 0.0 |
| 224.96608 | 1993.0 |
| 250.25261 | 0.0 |
| 419.00363 | 0.0 |
| 593.3971 | 1958.0 |
| 269.89669 | 1999.0 |
| 235.12771 | 2009.0 |
| 180.76689 | 0.0 |
| 304.03873 | 2004.0 |
| 253.36118 | 2006.0 |
| 311.74485 | 2006.0 |
| 353.43628 | 0.0 |
| 337.00526 | 0.0 |
| 305.00526 | 2006.0 |
| 113.76281 | 2007.0 |
| 379.74159 | 0.0 |
| 258.76853 | 1993.0 |
| 157.64853 | 0.0 |
| 352.28689 | 0.0 |
| 221.51791 | 0.0 |
| 249.44281 | 0.0 |
| 205.42649 | 0.0 |
| 166.922 | 0.0 |
| 250.25261 | 0.0 |
| 224.73098 | 2003.0 |
| 316.83873 | 2002.0 |
| 269.34812 | 2007.0 |
| 188.02893 | 0.0 |
| 276.87138 | 2001.0 |
| 263.02649 | 0.0 |
| 320.44363 | 0.0 |
| 531.43465 | 2005.0 |
| 126.85016 | 2008.0 |
| 232.01914 | 0.0 |
| 243.87873 | 0.0 |
| 288.60036 | 2004.0 |
| 817.57995 | 2007.0 |
| 200.9073 | 0.0 |
| 229.48526 | 2009.0 |
| 263.65342 | 1971.0 |
| 209.71057 | 2008.0 |
| 430.54975 | 2007.0 |
| 531.9571 | 0.0 |
| 277.39383 | 0.0 |
| 253.41342 | 1999.0 |
| 538.5922 | 0.0 |
| 187.34975 | 0.0 |
| 189.67465 | 2006.0 |
| 247.66649 | 0.0 |
| 196.15302 | 2008.0 |
| 248.45016 | 0.0 |
| 266.26567 | 2005.0 |
| 174.41914 | 0.0 |
| 241.21424 | 1996.0 |
| 213.39383 | 0.0 |
| 201.66485 | 1956.0 |
| 141.16526 | 0.0 |
| 198.76526 | 2010.0 |
| 234.03057 | 2002.0 |
| 293.77261 | 0.0 |
| 149.83791 | 0.0 |
| 193.09669 | 0.0 |
| 416.62649 | 2007.0 |
| 206.18404 | 2008.0 |
| 292.15302 | 0.0 |
| 209.55383 | 1997.0 |
| 303.46404 | 0.0 |
| 284.31628 | 0.0 |
| 209.34485 | 0.0 |
| 131.34322 | 2010.0 |
| 127.16363 | 0.0 |
| 228.98893 | 1983.0 |
| 18.18077 | 0.0 |
| 202.762 | 1999.0 |
| 475.21914 | 1989.0 |
| 434.52036 | 2002.0 |
| 306.36363 | 0.0 |
| 251.84608 | 2007.0 |
| 392.80281 | 1999.0 |
| 191.63383 | 0.0 |
| 207.90812 | 0.0 |
| 298.86649 | 0.0 |
| 195.36934 | 0.0 |
| 236.06812 | 1995.0 |
| 315.76771 | 2009.0 |
| 214.5171 | 0.0 |
| 140.90404 | 0.0 |
| 147.66975 | 0.0 |
| 230.50404 | 0.0 |
| 259.99628 | 2010.0 |
| 234.70975 | 1994.0 |
| 191.97342 | 1992.0 |
| 305.6322 | 0.0 |
| 197.53751 | 1997.0 |
| 152.05832 | 0.0 |
| 360.82893 | 1998.0 |
| 440.37179 | 0.0 |
| 211.09506 | 2009.0 |
| 362.60526 | 1998.0 |
| 364.64281 | 1997.0 |
| 267.12771 | 0.0 |
| 380.81261 | 2007.0 |
| 248.13669 | 1995.0 |
| 253.20444 | 0.0 |
| 244.03546 | 0.0 |
| 159.13751 | 0.0 |
| 246.12526 | 0.0 |
| 40.95955 | 2005.0 |
| 200.04526 | 2007.0 |
| 155.08853 | 0.0 |
| 144.66567 | 0.0 |
| 170.86649 | 0.0 |
| 286.71955 | 2001.0 |
| 333.19138 | 1996.0 |
| 542.1971 | 0.0 |
| 222.37995 | 0.0 |
| 195.68281 | 2003.0 |
| 440.00608 | 0.0 |
| 223.08526 | 0.0 |
| 378.98404 | 0.0 |
| 91.45424 | 1983.0 |
| 114.65098 | 2009.0 |
| 218.80118 | 0.0 |
| 242.36363 | 0.0 |
| 143.0722 | 1962.0 |
| 242.78159 | 2007.0 |
| 256.31302 | 0.0 |
| 244.37506 | 0.0 |
| 36.54485 | 2007.0 |
| 401.94567 | 1999.0 |
| 178.65098 | 2003.0 |
| 277.002 | 2009.0 |
| 288.70485 | 2002.0 |
| 228.91057 | 2006.0 |
| 204.06812 | 0.0 |
| 212.40118 | 0.0 |
| 224.31302 | 2008.0 |
| 195.7873 | 1985.0 |
| 244.63628 | 2005.0 |
| 241.81506 | 0.0 |
| 224.10404 | 2001.0 |
| 132.75383 | 2008.0 |
| 113.3971 | 0.0 |
| 237.03465 | 0.0 |
| 162.58567 | 1987.0 |
| 247.24853 | 2008.0 |
| 285.30893 | 0.0 |
| 318.24934 | 0.0 |
| 375.53587 | 2007.0 |
| 188.78649 | 0.0 |
| 108.79955 | 0.0 |
| 270.91546 | 0.0 |
| 249.23383 | 0.0 |
| 192.80934 | 1984.0 |
| 295.20934 | 0.0 |
| 177.84118 | 2006.0 |
| 242.6771 | 0.0 |
| 245.28934 | 1999.0 |
| 105.61261 | 0.0 |
| 329.29914 | 0.0 |
| 207.46404 | 0.0 |
| 225.51465 | 2007.0 |
| 123.8722 | 0.0 |
| 270.10567 | 2008.0 |
| 174.86322 | 0.0 |
| 377.28608 | 0.0 |
| 220.18567 | 2005.0 |
| 1190.53016 | 0.0 |
| 1518.65424 | 0.0 |
| 438.64771 | 2008.0 |
| 344.842 | 2001.0 |
| 76.48608 | 1994.0 |
| 174.52363 | 2002.0 |
| 581.14567 | 0.0 |
| 177.68444 | 0.0 |
| 125.962 | 0.0 |
| 160.39138 | 2006.0 |
| 211.27791 | 0.0 |
| 182.88281 | 0.0 |
| 261.53751 | 2005.0 |
| 285.80526 | 0.0 |
| 263.44444 | 1991.0 |
| 133.32853 | 1998.0 |
| 313.99138 | 1990.0 |
| 199.18322 | 0.0 |
| 200.98567 | 0.0 |
| 170.84036 | 2009.0 |
| 194.48118 | 0.0 |
| 241.65832 | 0.0 |
| 245.15873 | 1970.0 |
| 262.66077 | 2002.0 |
| 307.46077 | 1999.0 |
| 295.20934 | 0.0 |
| 259.52608 | 0.0 |
| 347.19302 | 0.0 |
| 206.91546 | 0.0 |
| 399.51628 | 2008.0 |
| 271.25506 | 0.0 |
| 172.7473 | 1991.0 |
| 231.65342 | 1993.0 |
| 208.1171 | 1991.0 |
| 195.76118 | 1983.0 |
| 723.27791 | 1970.0 |
| 282.95791 | 0.0 |
| 153.12934 | 0.0 |
| 207.15057 | 0.0 |
| 174.41914 | 0.0 |
| 269.29587 | 2005.0 |
| 275.3824 | 0.0 |
| 149.41995 | 0.0 |
| 108.35546 | 1963.0 |
| 243.69587 | 0.0 |
| 308.27057 | 1996.0 |
| 204.90404 | 2007.0 |
| 311.24853 | 2001.0 |
| 164.77995 | 0.0 |
| 449.51465 | 0.0 |
| 140.93016 | 0.0 |
| 165.22404 | 2005.0 |
| 53.26322 | 2007.0 |
| 218.80118 | 2005.0 |
| 300.85179 | 1991.0 |
| 388.75383 | 2007.0 |
| 150.77832 | 1970.0 |
| 293.11955 | 0.0 |
| 177.71057 | 0.0 |
| 184.11057 | 2005.0 |
| 225.17506 | 2009.0 |
| 272.19546 | 2002.0 |
| 157.67465 | 0.0 |
| 204.61669 | 0.0 |
| 93.98812 | 0.0 |
| 204.45995 | 0.0 |
| 307.1473 | 0.0 |
| 347.0624 | 1970.0 |
| 184.73751 | 2005.0 |
| 146.65098 | 0.0 |
| 513.90649 | 2001.0 |
| 293.85098 | 1970.0 |
| 121.73016 | 2003.0 |
| 86.72608 | 0.0 |
| 171.25832 | 2007.0 |
| 264.95955 | 2007.0 |
| 411.68934 | 0.0 |
| 190.79791 | 1971.0 |
| 159.65995 | 0.0 |
| 162.89914 | 0.0 |
| 205.97506 | 2006.0 |
| 204.59057 | 0.0 |
| 117.02812 | 0.0 |
| 135.28771 | 0.0 |
| 163.65669 | 0.0 |
| 254.95465 | 0.0 |
| 178.31138 | 2001.0 |
| 150.77832 | 2001.0 |
| 410.53995 | 2001.0 |
| 222.30159 | 0.0 |
| 314.74893 | 0.0 |
| 233.11628 | 0.0 |
| 226.21995 | 0.0 |
| 441.67791 | 0.0 |
| 120.99873 | 2009.0 |
| 157.75302 | 0.0 |
| 203.65016 | 0.0 |
| 287.73832 | 0.0 |
| 226.7424 | 1997.0 |
| 69.56363 | 1993.0 |
| 174.52363 | 2007.0 |
| 363.67628 | 0.0 |
| 136.48934 | 0.0 |
| 390.60853 | 0.0 |
| 284.60363 | 0.0 |
| 291.81342 | 1990.0 |
| 502.7522 | 0.0 |
| 197.27628 | 0.0 |
| 329.53424 | 2009.0 |
| 340.1922 | 2003.0 |
| 170.94485 | 0.0 |
| 113.57995 | 0.0 |
| 205.24363 | 2009.0 |
| 169.22077 | 1994.0 |
| 285.70077 | 1980.0 |
| 221.23057 | 0.0 |
| 310.38649 | 0.0 |
| 353.48853 | 2008.0 |
| 415.92118 | 0.0 |
| 150.59546 | 0.0 |
| 236.90404 | 0.0 |
| 227.42159 | 1981.0 |
| 229.8771 | 1995.0 |
| 359.3922 | 0.0 |
| 403.17342 | 1998.0 |
| 296.59383 | 1997.0 |
| 117.65506 | 0.0 |
| 241.3971 | 0.0 |
| 34.92526 | 0.0 |
| 188.31628 | 0.0 |
| 409.02485 | 2002.0 |
| 335.5424 | 0.0 |
| 354.63791 | 0.0 |
| 213.31546 | 2007.0 |
| 238.62812 | 0.0 |
| 193.33179 | 1972.0 |
| 225.33179 | 0.0 |
| 166.84363 | 0.0 |
| 79.96036 | 1990.0 |
| 158.69342 | 2000.0 |
| 176.53506 | 0.0 |
| 347.61098 | 1999.0 |
| 106.39628 | 1994.0 |
| 147.93098 | 0.0 |
| 446.92853 | 0.0 |
| 360.22812 | 0.0 |
| 214.56934 | 0.0 |
| 325.35465 | 0.0 |
| 413.23057 | 0.0 |
| 218.04363 | 2001.0 |
| 215.30077 | 2002.0 |
| 57.44281 | 0.0 |
| 247.48363 | 2006.0 |
| 793.25995 | 0.0 |
| 467.3824 | 0.0 |
| 327.00036 | 1984.0 |
| 232.72444 | 2006.0 |
| 251.68934 | 0.0 |
| 197.3024 | 0.0 |
| 193.88036 | 0.0 |
| 383.32036 | 2004.0 |
| 269.71383 | 0.0 |
| 255.05914 | 0.0 |
| 337.18812 | 2009.0 |
| 240.92689 | 0.0 |
| 206.18404 | 2002.0 |
| 143.22893 | 0.0 |
| 244.27057 | 1980.0 |
| 83.56526 | 0.0 |
| 428.40771 | 0.0 |
| 261.11955 | 2007.0 |
| 208.37832 | 2008.0 |
| 369.78893 | 0.0 |
| 47.17669 | 2006.0 |
| 239.3073 | 0.0 |
| 17.37098 | 1993.0 |
| 257.04444 | 0.0 |
| 198.63465 | 0.0 |
| 208.40444 | 2002.0 |
| 338.28526 | 2005.0 |
| 175.15057 | 0.0 |
| 234.97098 | 0.0 |
| 275.06893 | 0.0 |
| 186.46159 | 0.0 |
| 201.74322 | 0.0 |
| 237.58322 | 0.0 |
| 219.402 | 0.0 |
| 461.29587 | 0.0 |
| 196.67546 | 0.0 |
| 290.63791 | 0.0 |
| 328.22812 | 0.0 |
| 260.64934 | 1981.0 |
| 245.83791 | 0.0 |
| 97.54077 | 0.0 |
| 248.0322 | 0.0 |
| 175.33342 | 1998.0 |
| 199.57506 | 0.0 |
| 229.45914 | 2005.0 |
| 902.26893 | 2000.0 |
| 271.12444 | 1991.0 |
| 211.17342 | 0.0 |
| 179.3824 | 1967.0 |
| 156.96934 | 0.0 |
| 281.0771 | 1998.0 |
| 291.97016 | 0.0 |
| 392.85506 | 0.0 |
| 223.00689 | 0.0 |
| 269.94893 | 1987.0 |
| 36.64934 | 1996.0 |
| 309.26322 | 0.0 |
| 178.41587 | 0.0 |
| 206.75873 | 2006.0 |
| 155.68934 | 1971.0 |
| 254.71955 | 1993.0 |
| 133.11955 | 0.0 |
| 260.362 | 0.0 |
| 135.28771 | 1984.0 |
| 158.27546 | 2005.0 |
| 154.93179 | 0.0 |
| 205.84444 | 2005.0 |
| 276.21832 | 0.0 |
| 193.61914 | 0.0 |
| 153.73016 | 1997.0 |
| 389.11955 | 0.0 |
| 195.23873 | 2007.0 |
| 210.72934 | 1995.0 |
| 336.06485 | 0.0 |
| 263.02649 | 0.0 |
| 230.26893 | 2001.0 |
| 40.6722 | 2007.0 |
| 255.92118 | 2009.0 |
| 305.60608 | 1995.0 |
| 177.8673 | 0.0 |
| 361.11628 | 0.0 |
| 357.66812 | 0.0 |
| 196.49261 | 2004.0 |
| 218.40934 | 1992.0 |
| 91.58485 | 0.0 |
| 185.25995 | 0.0 |
| 282.80118 | 0.0 |
| 244.68853 | 0.0 |
| 215.40526 | 1993.0 |
| 211.19955 | 1978.0 |
| 327.75791 | 0.0 |
| 510.40608 | 2005.0 |
| 212.74077 | 2009.0 |
| 120.86812 | 2008.0 |
| 507.08853 | 2001.0 |
| 265.11628 | 0.0 |
| 183.06567 | 0.0 |
| 199.54893 | 1982.0 |
| 41.92608 | 0.0 |
| 164.75383 | 0.0 |
| 267.33669 | 0.0 |
| 208.74404 | 1984.0 |
| 253.09995 | 2007.0 |
| 244.50567 | 0.0 |
| 195.73506 | 2007.0 |
| 160.07791 | 2007.0 |
| 327.70567 | 0.0 |
| 174.86322 | 0.0 |
| 272.92689 | 2005.0 |
| 251.53261 | 0.0 |
| 216.99873 | 0.0 |
| 195.3171 | 0.0 |
| 247.11791 | 0.0 |
| 101.3024 | 0.0 |
| 315.97669 | 2003.0 |
| 449.67138 | 0.0 |
| 173.16526 | 1998.0 |
| 394.44853 | 0.0 |
| 226.69016 | 2007.0 |
| 219.11465 | 0.0 |
| 240.92689 | 1997.0 |
| 227.91791 | 1999.0 |
| 119.84934 | 0.0 |
| 109.92281 | 1997.0 |
| 116.08771 | 0.0 |
| 187.71546 | 1975.0 |
| 191.65995 | 0.0 |
| 116.32281 | 1979.0 |
| 482.45506 | 0.0 |
| 262.71302 | 2003.0 |
| 208.97914 | 2005.0 |
| 209.81506 | 1975.0 |
| 129.85424 | 2002.0 |
| 219.0624 | 0.0 |
| 500.4273 | 2010.0 |
| 224.7571 | 0.0 |
| 274.85995 | 2003.0 |
| 145.162 | 1993.0 |
| 211.27791 | 0.0 |
| 167.49669 | 1981.0 |
| 415.7122 | 0.0 |
| 346.33098 | 0.0 |
| 399.69914 | 1999.0 |
| 136.56771 | 0.0 |
Exercises
- Why do you think average song durations increase dramatically in 70's?
- Add error bars with standard deviation around each average point in the plot.
- How did average loudness change over time?
- How did tempo change over time?
- What other aspects of songs can you explore with this technique?
Sampling and visualizing
You can dive deep into Scala visualisations here: - https://docs.databricks.com/notebooks/visualizations/charts-and-graphs-scala.html
You can also use R and Python for visualisations in the same notebook: - https://docs.databricks.com/notebooks/visualizations/index.html
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
Stage 3: Modeling Songs via k-means

This is the third step into our project. In the first step we parsed raw text files and created a table. Then we explored different aspects of data and learned that things have been changing over time. In this step we attempt to gain deeper understanding of our data by categorizing (a.k.a. clustering) our data. For the sake of training we pick a fairly simple model based on only three parameters. We leave more sophisticated modeling as exercies to the reader
We pick the most commonly used and simplest clustering algorithm (KMeans) for our job. The SparkML KMeans implementation expects input in a vector column. Fortunately, there are already utilities in SparkML that can help us convert existing columns in our table to a vector field. It is called VectorAssembler. Here we import that functionality and use it to create a new DataFrame
// Let's quickly do everything to register the tempView of the table here
// this is a case class for our row objects
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
def parseLine(line: String): Song = {
// this is robust parsing by try-catching type exceptions
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
// splitting the sting of each line by the delimiter TAB character '\t'
val tokens = line.split("\t")
// making song objects
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*")
// .. fill in comment
val df = dataRDD.map(parseLine).toDF
// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[106] at textFile at command-2971213210277067:32
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
import org.apache.spark.ml.feature.VectorAssembler
val trainingData = new VectorAssembler()
.setInputCols(Array("duration", "tempo", "loudness"))
.setOutputCol("features")
.transform(table("songsTable"))
import org.apache.spark.ml.feature.VectorAssembler
trainingData: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 19 more fields]
All we have done above with the VectorAssembler method is:
- created a DataFrame called
trainingData - that
transformed ourtablecalledsongsTable - by adding an output column named
featuresusingsetOutputCol("features") - that was obtained from an
Arrayof thesongsTable's columns namedduration,tempoandloudnessusingsetInputCols(Array("duration", "tempo", "loudness")).
trainingData.take(3) // see first 3 rows of trainingData DataFrame, notice the vectors in the last column
res3: Array[org.apache.spark.sql.Row] = Array([AR81V6H1187FB48872,0.0,0.0,,Earl Sixteen,213.7073,0.0,11,0.419,-12.106,Soldier of Jah Army,0.0,SOVNZSZ12AB018A9B8,208.289,125.882,1.0,0.0,Rastaman,2003.0,-1,[213.7073,125.882,-12.106]], [ARVVZQP11E2835DBCB,0.0,0.0,,Wavves,133.25016,0.0,0,0.282,0.596,Wavvves,0.471578247701,SOJTQHQ12A8C143C5F,128.116,89.519,1.0,0.0,I Want To See You (And Go To The Movies),2009.0,-1,[133.25016,89.519,0.596]], [ARFG9M11187FB3BBCB,0.0,0.0,Nashua USA,C-Side,247.32689,0.0,9,0.612,-4.896,Santa Festival Compilation 2008 vol.1,0.0,SOAJSQL12AB0180501,242.196,171.278,5.0,1.0,Loose on the Dancefloor,0.0,225261,[247.32689,171.278,-4.896]])
Transformers
A Transformer is an abstraction that includes feature transformers and learned models. Technically, a Transformer implements a method transform(), which converts one DataFrame into another, generally by appending one or more columns. For example:
- A feature transformer might take a DataFrame, read a column (e.g., text), map it into a new column (e.g., feature vectors), and output a new DataFrame with the mapped column appended.
- A learning model might take a DataFrame, read the column containing feature vectors, predict the label for each feature vector, and output a new DataFrame with predicted labels appended as a column.
Estimators
An Estimator abstracts the concept of a learning algorithm or any algorithm that fits or trains on data.
Technically, an Estimator implements a method fit(), which accepts a DataFrame and produces a Model, which is a Transformer.
For example, a learning algorithm such as LogisticRegression is an Estimator, and calling fit() trains a LogisticRegressionModel, which is a Model and hence a Transformer.
display(trainingData.select("duration", "tempo", "loudness", "features").limit(5)) // see features in more detail
Demonstration of the standard algorithm
(1) (2)
(3)
(4)
- k initial "means" (in this case k=3) are randomly generated within the data domain (shown in color).
- k clusters are created by associating every observation with the nearest mean. The partitions here represent the Voronoi diagram generated by the means.
- The centroid of each of the k clusters becomes the new mean.
- Steps 2 and 3 are repeated until local convergence has been reached.
The "assignment" step 2 is also referred to as expectation step, the "update step" 3 as maximization step, making this algorithm a variant of the generalized expectation-maximization algorithm.
Caveats: As k-means is a heuristic algorithm, there is no guarantee that it will converge to the global optimum, and the result may depend on the initial clusters. As the algorithm is usually very fast, it is common to run it multiple times with different starting conditions. However, in the worst case, k-means can be very slow to converge. For more details see https://en.wikipedia.org/wiki/K-means_clustering that is also embedded in-place below.
CAUTION!
Iris flower data set, clustered using
- k-means (left) and
- true species in the data set (right).
k-means clustering result for the Iris flower data set and actual species visualized using ELKI. Cluster means are marked using larger, semi-transparent symbols.
Note that k-means is non-determinicstic, so results vary. Cluster means are visualized using larger, semi-transparent markers. The visualization was generated using ELKI.
With some cautionary tales we go ahead with applying k-means to our dataset next.
We can now pass this new DataFrame to the KMeans model and ask it to categorize different rows in our data to two different classes (setK(2)). We place the model in a immutable value named model.
Note: This command performs multiple spark jobs (one job per iteration in the KMeans algorithm). You will see the progress bar starting over and over again.
import org.apache.spark.ml.clustering.KMeans
val model = new KMeans().setK(2).fit(trainingData) // 37 seconds in 5 workers cluster
import org.apache.spark.ml.clustering.KMeans
model: org.apache.spark.ml.clustering.KMeansModel = KMeansModel: uid=kmeans_a1617b1902bd, k=2, distanceMeasure=euclidean, numFeatures=3
//model. // uncomment and place cursor next to . and hit Tab to see all methods on model
model.clusterCenters // get cluster centres
res5: Array[org.apache.spark.ml.linalg.Vector] = Array([208.72069181761955,124.38376303683947,-9.986137113920133], [441.1413218945455,123.00973381818183,-10.560375818181816])
val modelTransformed = model.transform(trainingData) // to get predictions as last column
modelTransformed: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 20 more fields]
Remember that ML Pipelines works with DataFrames. So, our trainingData and modelTransformed are both DataFrames
trainingData.printSchema
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
|-- features: vector (nullable = true)
modelTransformed.printSchema
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
|-- features: vector (nullable = true)
|-- prediction: integer (nullable = false)
- The column
featuresthat we specified as output column to ourVectorAssemblercontains the features - The new column
predictionin modelTransformed contains the predicted output
val transformed = modelTransformed.select("duration", "tempo", "loudness", "prediction")
transformed: org.apache.spark.sql.DataFrame = [duration: double, tempo: double ... 2 more fields]
To comfortably visualize the data we produce a random sample. Remember the display() function? We can use it to produce a nicely rendered table of transformed DataFrame.
// just sampling the fraction 0.005 of all the rows at random,
// 'false' argument to sample is for sampling without replacement
display(transformed.sample(false, fraction = 0.005, 12988934L))
| duration | tempo | loudness | prediction |
|---|---|---|---|
| 265.97832 | 129.987 | -5.065 | 0.0 |
| 306.36363 | 127.529 | -8.273 | 0.0 |
| 195.73506 | 115.191 | -6.327 | 0.0 |
| 257.82812 | 92.892 | -13.621 | 0.0 |
| 177.55383 | 149.853 | -10.969 | 0.0 |
| 224.9922 | 97.98 | -5.921 | 0.0 |
| 528.09098 | 160.022 | -5.956 | 1.0 |
| 213.68118 | 100.219 | -4.528 | 0.0 |
| 35.52608 | 121.349 | -18.721 | 0.0 |
| 213.68118 | 120.581 | -10.633 | 0.0 |
| 300.53832 | 139.017 | -13.319 | 0.0 |
| 138.81424 | 160.044 | -11.138 | 0.0 |
| 413.962 | 89.588 | -13.143 | 1.0 |
| 292.17914 | 188.543 | -5.389 | 0.0 |
| 419.34322 | 140.008 | -5.389 | 1.0 |
| 261.61587 | 152.951 | -12.447 | 0.0 |
| 168.64608 | 99.617 | -8.164 | 0.0 |
| 446.27546 | 196.005 | -6.621 | 1.0 |
| 343.92771 | 122.877 | -10.264 | 1.0 |
| 261.69424 | 145.129 | -4.733 | 0.0 |
| 408.47628 | 61.259 | -11.244 | 1.0 |
| 243.3824 | 161.778 | -6.88 | 0.0 |
| 184.00608 | 111.461 | -9.788 | 0.0 |
| 67.39546 | 188.723 | -3.239 | 0.0 |
| 165.51138 | 81.125 | -18.692 | 0.0 |
| 214.85669 | 144.174 | -17.989 | 0.0 |
| 185.96526 | 186.897 | -6.041 | 0.0 |
| 170.05669 | 75.356 | -16.486 | 0.0 |
| 193.72363 | 117.131 | -9.822 | 0.0 |
| 227.23873 | 91.953 | -11.221 | 0.0 |
| 177.6322 | 150.962 | -7.288 | 0.0 |
| 117.21098 | 100.021 | -10.782 | 0.0 |
| 422.42567 | 91.214 | -8.872 | 1.0 |
| 145.162 | 95.388 | -10.166 | 0.0 |
| 242.93832 | 112.015 | -7.135 | 0.0 |
| 366.00118 | 65.05 | -13.768 | 1.0 |
| 601.15546 | 70.623 | -11.179 | 1.0 |
| 196.17914 | 121.563 | -5.434 | 0.0 |
| 399.35955 | 124.963 | -3.415 | 1.0 |
| 287.73832 | 161.648 | -14.721 | 0.0 |
| 285.09995 | 132.86 | -10.23 | 0.0 |
| 358.32118 | 132.874 | -6.071 | 1.0 |
| 407.71873 | 85.996 | -10.103 | 1.0 |
| 463.25506 | 95.083 | -6.4 | 1.0 |
| 295.91465 | 93.515 | -8.987 | 0.0 |
| 273.8673 | 190.044 | -10.556 | 0.0 |
| 280.73751 | 113.363 | -6.893 | 0.0 |
| 142.65424 | 206.067 | -7.996 | 0.0 |
| 197.38077 | 84.023 | -10.964 | 0.0 |
| 226.24608 | 166.768 | -5.941 | 0.0 |
| 152.47628 | 153.528 | -7.393 | 0.0 |
| 315.32363 | 139.919 | -7.438 | 0.0 |
| 329.03791 | 180.059 | -6.473 | 1.0 |
| 296.88118 | 137.976 | -5.49 | 0.0 |
| 250.77506 | 84.543 | -18.058 | 0.0 |
| 48.14322 | 52.814 | -11.617 | 0.0 |
| 290.16771 | 141.24 | -14.009 | 0.0 |
| 406.04689 | 133.997 | -9.685 | 1.0 |
| 124.57751 | 161.237 | -12.314 | 0.0 |
| 280.45016 | 154.003 | -8.094 | 0.0 |
| 472.31955 | 219.371 | -6.08 | 1.0 |
| 267.4673 | 152.123 | -18.457 | 0.0 |
| 216.21506 | 88.022 | -7.096 | 0.0 |
| 31.00689 | 237.737 | -11.538 | 0.0 |
| 283.21914 | 132.934 | -11.153 | 0.0 |
| 284.21179 | 110.585 | -15.384 | 0.0 |
| 154.87955 | 157.881 | -4.377 | 0.0 |
| 431.46404 | 113.905 | -8.651 | 1.0 |
| 215.87546 | 124.572 | -7.67 | 0.0 |
| 161.43628 | 43.884 | -14.936 | 0.0 |
| 29.09995 | 135.146 | -1.837 | 0.0 |
| 301.68771 | 123.672 | -9.293 | 0.0 |
| 170.10893 | 124.022 | -9.589 | 0.0 |
| 281.96526 | 63.544 | -8.25 | 0.0 |
| 484.54485 | 149.565 | -5.501 | 1.0 |
| 135.1571 | 92.749 | -7.881 | 0.0 |
| 180.45342 | 137.899 | -5.246 | 0.0 |
| 142.44526 | 85.406 | -11.305 | 0.0 |
| 209.3971 | 93.233 | -13.079 | 0.0 |
| 319.9473 | 122.936 | -5.98 | 0.0 |
| 55.82322 | 249.325 | -8.656 | 0.0 |
| 192.88771 | 124.006 | -5.745 | 0.0 |
| 624.14322 | 128.988 | -7.872 | 1.0 |
| 188.49914 | 107.264 | -13.24 | 0.0 |
| 148.32281 | 150.075 | -5.793 | 0.0 |
| 195.00363 | 49.31 | -14.54 | 0.0 |
| 130.61179 | 100.208 | -10.487 | 0.0 |
| 212.11383 | 111.048 | -4.749 | 0.0 |
| 204.56444 | 92.076 | -7.906 | 0.0 |
| 292.04853 | 135.932 | -9.004 | 0.0 |
| 377.83465 | 73.336 | -13.457 | 1.0 |
| 246.96118 | 116.761 | -8.117 | 0.0 |
| 90.30485 | 39.3 | -15.444 | 0.0 |
| 201.92608 | 123.558 | -9.857 | 0.0 |
| 282.06975 | 136.09 | -6.202 | 0.0 |
| 194.55955 | 119.488 | -16.454 | 0.0 |
| 318.4322 | 140.467 | -6.375 | 0.0 |
| 55.45751 | 121.476 | -7.891 | 0.0 |
| 275.30404 | 90.024 | -6.191 | 0.0 |
| 422.24281 | 131.994 | -8.786 | 1.0 |
| 269.47873 | 88.921 | -4.419 | 0.0 |
| 186.01751 | 55.271 | -16.153 | 0.0 |
| 221.17832 | 94.967 | -6.591 | 0.0 |
| 275.66975 | 94.999 | -16.843 | 0.0 |
| 335.28118 | 90.129 | -13.33 | 1.0 |
| 288.88771 | 102.96 | -10.967 | 0.0 |
| 190.71955 | 88.269 | -13.167 | 0.0 |
| 211.17342 | 126.741 | -10.398 | 0.0 |
| 116.92363 | 140.09 | -7.578 | 0.0 |
| 310.282 | 181.753 | -17.61 | 0.0 |
| 184.21506 | 112.336 | -8.764 | 0.0 |
| 248.21506 | 101.988 | -6.487 | 0.0 |
| 116.00934 | 84.793 | -9.057 | 0.0 |
| 299.88526 | 143.66 | -7.279 | 0.0 |
| 1376.78322 | 107.066 | -7.893 | 1.0 |
| 182.85669 | 191.559 | -10.367 | 0.0 |
| 238.2624 | 48.592 | -9.687 | 0.0 |
| 239.5424 | 105.47 | -9.258 | 0.0 |
| 195.16036 | 127.911 | -7.054 | 0.0 |
| 149.73342 | 118.643 | -10.526 | 0.0 |
| 424.30649 | 62.8 | -22.35 | 1.0 |
| 221.77914 | 104.983 | -7.908 | 0.0 |
| 407.7971 | 126.983 | -11.675 | 1.0 |
| 175.04608 | 120.542 | -17.484 | 0.0 |
| 306.65098 | 120.0 | -18.973 | 0.0 |
| 315.0624 | 85.82 | -11.858 | 0.0 |
| 88.5024 | 211.429 | -11.543 | 0.0 |
| 101.61587 | 19.215 | -26.402 | 0.0 |
| 132.04853 | 186.123 | -7.21 | 0.0 |
| 146.25914 | 200.16 | -15.88 | 0.0 |
| 147.3824 | 104.658 | -8.836 | 0.0 |
| 156.26404 | 135.353 | -8.245 | 0.0 |
| 66.2722 | 115.129 | -16.416 | 0.0 |
| 247.43138 | 124.991 | -11.79 | 0.0 |
| 282.90567 | 115.472 | -12.448 | 0.0 |
| 401.05751 | 125.015 | -9.465 | 1.0 |
| 156.08118 | 88.128 | -16.916 | 0.0 |
| 198.08608 | 133.961 | -8.055 | 0.0 |
| 214.09914 | 84.353 | -12.991 | 0.0 |
| 269.73995 | 161.953 | -3.091 | 0.0 |
| 199.44444 | 169.922 | -6.241 | 0.0 |
| 149.65506 | 190.802 | -30.719 | 0.0 |
| 227.23873 | 120.14 | -12.39 | 0.0 |
To generate a scatter plot matrix, click on the plot button bellow the table and select scatter. That will transform your table to a scatter plot matrix. It automatically picks all numeric columns as values. To include predicted clusters, click on Plot Options and drag prediction to the list of Keys. You will get the following plot. On the diagonal panels you see the PDF of marginal distribution of each variable. Non-diagonal panels show a scatter plot between variables of the two variables of the row and column. For example the top right panel shows the scatter plot between duration and loudness. Each point is colored according to the cluster it is assigned to.
display(transformed.sample(false, fraction = 0.01)) // try fraction=1.0 as this dataset is small
| duration | tempo | loudness | prediction |
|---|---|---|---|
| 189.93587 | 134.957 | -12.934 | 0.0 |
| 206.44526 | 135.26 | -11.146 | 0.0 |
| 295.65342 | 137.904 | -7.308 | 0.0 |
| 158.1971 | 174.831 | -12.544 | 0.0 |
| 259.99628 | 171.094 | -12.057 | 0.0 |
| 277.002 | 88.145 | -6.039 | 0.0 |
| 1518.65424 | 65.003 | -19.182 | 1.0 |
| 121.73016 | 85.041 | -8.193 | 0.0 |
| 205.97506 | 106.952 | -6.572 | 0.0 |
| 206.18404 | 89.447 | -9.535 | 0.0 |
| 202.52689 | 134.13 | -6.535 | 0.0 |
| 259.02975 | 97.948 | -11.276 | 0.0 |
| 308.53179 | 80.017 | -12.277 | 0.0 |
| 151.71873 | 117.179 | -12.394 | 0.0 |
| 165.56363 | 99.187 | -11.414 | 0.0 |
| 370.83383 | 67.495 | -20.449 | 1.0 |
| 238.65424 | 100.071 | -9.276 | 0.0 |
| 163.7873 | 119.924 | -5.941 | 0.0 |
| 279.32689 | 67.371 | -26.67 | 0.0 |
| 353.85424 | 68.405 | -8.105 | 1.0 |
| 227.23873 | 117.62 | -7.874 | 0.0 |
| 195.49995 | 85.916 | -8.828 | 0.0 |
| 322.45506 | 135.826 | -5.034 | 0.0 |
| 235.88526 | 109.773 | -10.919 | 0.0 |
| 235.15383 | 163.924 | -9.666 | 0.0 |
| 253.67465 | 125.014 | -7.171 | 0.0 |
| 415.03302 | 71.991 | -15.224 | 1.0 |
| 190.85016 | 152.805 | -2.871 | 0.0 |
| 290.76853 | 84.841 | -6.161 | 0.0 |
| 346.8273 | 129.981 | -10.377 | 1.0 |
| 174.70649 | 143.446 | -11.994 | 0.0 |
| 170.84036 | 70.033 | -7.782 | 0.0 |
| 517.66812 | 151.948 | -12.363 | 1.0 |
| 62.06649 | 154.406 | -9.387 | 0.0 |
| 505.96526 | 90.59 | -19.848 | 1.0 |
| 480.86159 | 0.0 | -11.707 | 1.0 |
| 330.13506 | 204.534 | -9.85 | 1.0 |
| 223.99955 | 110.3 | -12.339 | 0.0 |
| 200.98567 | 89.215 | -3.858 | 0.0 |
| 158.09261 | 178.826 | -13.681 | 0.0 |
| 186.46159 | 110.102 | -16.477 | 0.0 |
| 399.46404 | 125.03 | -10.786 | 1.0 |
| 118.64771 | 85.747 | -34.461 | 0.0 |
| 352.49587 | 91.139 | -9.13 | 1.0 |
| 150.15138 | 106.364 | -7.337 | 0.0 |
| 158.98077 | 124.54 | -17.405 | 0.0 |
| 122.17424 | 170.018 | -9.047 | 0.0 |
| 299.38893 | 91.976 | -6.266 | 0.0 |
| 197.95546 | 142.744 | -8.189 | 0.0 |
| 332.38159 | 89.071 | -5.08 | 1.0 |
| 309.78567 | 136.05 | -15.105 | 0.0 |
| 198.47791 | 82.546 | -12.594 | 0.0 |
| 254.17098 | 144.01 | -9.0 | 0.0 |
| 155.81995 | 233.022 | -8.978 | 0.0 |
| 176.53506 | 136.255 | -9.675 | 0.0 |
| 331.54567 | 221.511 | -13.778 | 1.0 |
| 140.72118 | 162.44 | -10.654 | 0.0 |
| 210.9122 | 120.039 | -5.391 | 0.0 |
| 197.27628 | 119.396 | -22.599 | 0.0 |
| 339.25179 | 96.842 | -16.207 | 1.0 |
| 355.97016 | 142.777 | -5.118 | 1.0 |
| 170.23955 | 55.107 | -17.654 | 0.0 |
| 360.01914 | 119.997 | -7.53 | 1.0 |
| 241.76281 | 101.938 | -3.765 | 0.0 |
| 84.92363 | 236.863 | -15.846 | 0.0 |
| 361.09016 | 120.121 | -5.861 | 1.0 |
| 262.37342 | 89.924 | -4.819 | 0.0 |
| 225.74975 | 139.994 | -11.505 | 0.0 |
| 246.9873 | 172.147 | -16.273 | 0.0 |
| 159.242 | 166.855 | -4.771 | 0.0 |
| 192.86159 | 148.297 | -13.423 | 0.0 |
| 140.90404 | 92.602 | -12.604 | 0.0 |
| 224.20853 | 71.879 | -16.615 | 0.0 |
| 235.78077 | 180.366 | -3.918 | 0.0 |
| 244.45342 | 105.077 | -5.004 | 0.0 |
| 210.57261 | 200.044 | -11.381 | 0.0 |
| 327.3922 | 112.023 | -11.341 | 1.0 |
| 168.38485 | 99.991 | -4.1 | 0.0 |
| 287.13751 | 137.154 | -12.948 | 0.0 |
| 148.63628 | 121.773 | -11.432 | 0.0 |
| 214.7522 | 124.437 | -5.834 | 0.0 |
| 197.43302 | 92.959 | -4.948 | 0.0 |
| 150.17751 | 112.0 | -5.968 | 0.0 |
| 152.86812 | 120.884 | -15.693 | 0.0 |
| 173.322 | 120.827 | -7.96 | 0.0 |
| 235.15383 | 85.519 | -8.172 | 0.0 |
| 299.41506 | 94.68 | -15.486 | 0.0 |
| 222.35383 | 156.046 | -8.885 | 0.0 |
| 235.85914 | 130.055 | -5.341 | 0.0 |
| 276.71465 | 170.059 | -2.926 | 0.0 |
| 136.07138 | 168.895 | -6.971 | 0.0 |
| 205.7922 | 129.57 | -6.113 | 0.0 |
| 268.72118 | 126.894 | -7.142 | 0.0 |
| 390.63465 | 232.08 | -5.493 | 1.0 |
| 242.59873 | 124.034 | -11.24 | 0.0 |
| 274.80771 | 108.178 | -21.456 | 0.0 |
| 157.25669 | 151.108 | -11.678 | 0.0 |
| 203.67628 | 140.02 | -5.939 | 0.0 |
| 302.47138 | 148.2 | -10.428 | 0.0 |
| 292.64934 | 106.006 | -3.538 | 0.0 |
| 245.002 | 135.997 | -10.6 | 0.0 |
| 181.57669 | 132.89 | -5.429 | 0.0 |
| 36.38812 | 65.132 | -12.621 | 0.0 |
| 656.14322 | 95.469 | -8.588 | 1.0 |
| 262.50404 | 88.025 | -4.359 | 0.0 |
| 261.95546 | 117.93 | -5.5 | 0.0 |
| 337.57995 | 122.08 | -6.658 | 1.0 |
| 373.08036 | 126.846 | -10.115 | 1.0 |
| 220.36853 | 157.934 | -13.192 | 0.0 |
| 357.22404 | 199.889 | -9.832 | 1.0 |
| 663.61424 | 143.681 | -9.066 | 1.0 |
| 155.08853 | 201.152 | -7.43 | 0.0 |
| 264.56771 | 133.7 | -11.767 | 0.0 |
| 262.53016 | 105.041 | -3.933 | 0.0 |
| 386.61179 | 132.644 | -8.34 | 1.0 |
| 224.41751 | 93.333 | -12.001 | 0.0 |
| 264.25424 | 117.547 | -6.134 | 0.0 |
| 12.82567 | 99.229 | -29.527 | 0.0 |
| 470.25587 | 135.99 | -7.403 | 1.0 |
| 213.7073 | 92.584 | -8.398 | 0.0 |
| 50.59873 | 108.989 | -10.006 | 0.0 |
| 273.162 | 114.014 | -8.527 | 0.0 |
| 285.1522 | 101.877 | -9.361 | 0.0 |
| 267.88526 | 123.903 | -10.177 | 0.0 |
| 334.54975 | 138.386 | -7.389 | 1.0 |
| 174.0273 | 95.084 | -21.944 | 0.0 |
| 242.15465 | 87.196 | -9.749 | 0.0 |
| 188.96934 | 188.967 | -3.288 | 0.0 |
| 480.67873 | 127.989 | -7.243 | 1.0 |
| 182.54322 | 72.894 | -17.837 | 0.0 |
| 231.1571 | 126.959 | -5.632 | 0.0 |
| 105.37751 | 136.156 | -13.607 | 0.0 |
| 125.88363 | 93.022 | -13.496 | 0.0 |
| 216.99873 | 151.866 | -7.47 | 0.0 |
| 176.50893 | 142.601 | -3.881 | 0.0 |
| 245.57669 | 123.186 | -3.609 | 0.0 |
| 191.37261 | 145.008 | -14.3 | 0.0 |
| 62.11873 | 80.961 | -12.453 | 0.0 |
| 274.80771 | 83.505 | -10.032 | 0.0 |
| 148.29669 | 92.154 | -13.542 | 0.0 |
| 241.47546 | 115.886 | -11.948 | 0.0 |
| 250.40934 | 100.001 | -18.335 | 0.0 |
| 154.56608 | 96.464 | -12.133 | 0.0 |
| 485.14567 | 84.633 | -8.812 | 1.0 |
| 307.22567 | 63.498 | -9.047 | 0.0 |
| 167.78404 | 122.92 | -13.528 | 0.0 |
| 345.10322 | 130.003 | -7.858 | 1.0 |
| 178.9122 | 111.13 | -7.33 | 0.0 |
| 237.29587 | 111.244 | -15.209 | 0.0 |
| 230.16444 | 74.335 | -12.244 | 0.0 |
| 230.50404 | 98.933 | -12.616 | 0.0 |
| 195.16036 | 131.037 | -12.769 | 0.0 |
| 130.01098 | 99.176 | -11.082 | 0.0 |
| 150.02077 | 97.501 | -2.945 | 0.0 |
| 239.64689 | 89.99 | -9.224 | 0.0 |
| 91.6371 | 71.187 | -16.523 | 0.0 |
| 136.88118 | 121.19 | -12.181 | 0.0 |
| 265.29914 | 167.132 | -13.859 | 0.0 |
| 166.84363 | 76.298 | -7.692 | 0.0 |
| 34.79465 | 109.386 | -13.591 | 0.0 |
| 344.29342 | 138.826 | -12.746 | 1.0 |
| 294.71302 | 123.963 | -15.225 | 0.0 |
| 232.9073 | 100.264 | -7.171 | 0.0 |
| 501.68118 | 126.947 | -8.972 | 1.0 |
| 163.00363 | 154.296 | -19.387 | 0.0 |
| 186.51383 | 89.989 | -5.338 | 0.0 |
| 279.30077 | 130.522 | -11.813 | 0.0 |
| 202.78812 | 79.515 | -26.44 | 0.0 |
| 123.45424 | 129.987 | -14.751 | 0.0 |
| 237.08689 | 82.135 | -6.347 | 0.0 |
| 239.75138 | 169.111 | -7.915 | 0.0 |
| 105.87383 | 145.028 | -2.414 | 0.0 |
| 250.46159 | 123.306 | -5.113 | 0.0 |
| 227.68281 | 132.51 | -6.661 | 0.0 |
| 487.44444 | 100.065 | -16.668 | 1.0 |
| 247.17016 | 165.886 | -8.476 | 0.0 |
| 194.92526 | 99.414 | -7.516 | 0.0 |
| 174.602 | 194.079 | -4.611 | 0.0 |
| 199.96689 | 148.78 | -9.657 | 0.0 |
| 278.7522 | 185.291 | -10.58 | 0.0 |
| 147.82649 | 88.634 | -3.83 | 0.0 |
| 166.1122 | 106.748 | -10.875 | 0.0 |
| 150.67383 | 120.367 | -14.628 | 0.0 |
| 237.53098 | 127.24 | -27.291 | 0.0 |
| 174.10567 | 124.185 | -6.983 | 0.0 |
| 84.84526 | 98.321 | -4.167 | 0.0 |
| 287.50322 | 121.821 | -9.951 | 0.0 |
| 142.23628 | 184.994 | -16.834 | 0.0 |
| 85.91628 | 180.788 | -9.972 | 0.0 |
| 217.23383 | 108.892 | -7.211 | 0.0 |
| 233.63873 | 113.878 | -6.394 | 0.0 |
| 291.94404 | 91.976 | -6.998 | 0.0 |
| 502.5171 | 140.015 | -5.285 | 1.0 |
| 316.02893 | 97.508 | -10.52 | 0.0 |
| 408.58077 | 176.977 | -15.701 | 1.0 |
| 244.13995 | 156.187 | -5.985 | 0.0 |
| 146.46812 | 140.032 | -13.347 | 0.0 |
| 131.05587 | 114.198 | -23.084 | 0.0 |
| 175.80363 | 137.052 | -7.147 | 0.0 |
| 230.53016 | 115.496 | -3.953 | 0.0 |
| 247.92771 | 90.092 | -10.103 | 0.0 |
| 207.04608 | 132.94 | -7.325 | 0.0 |
| 163.70893 | 77.698 | -18.806 | 0.0 |
| 179.722 | 81.884 | -40.036 | 0.0 |
| 214.02077 | 87.003 | -4.724 | 0.0 |
| 379.68934 | 127.986 | -8.715 | 1.0 |
| 173.322 | 74.929 | -3.089 | 0.0 |
| 263.81016 | 131.945 | -6.911 | 0.0 |
| 244.21832 | 109.004 | -3.762 | 0.0 |
| 220.1073 | 99.948 | -14.54 | 0.0 |
| 298.37016 | 87.591 | -31.42 | 0.0 |
| 273.97179 | 169.372 | -10.764 | 0.0 |
| 229.74649 | 117.184 | -6.174 | 0.0 |
| 383.68608 | 127.261 | -3.975 | 1.0 |
| 278.02077 | 47.779 | -4.486 | 0.0 |
| 135.75791 | 164.977 | -5.974 | 0.0 |
| 373.75955 | 129.057 | -8.391 | 1.0 |
| 272.40444 | 162.882 | -14.916 | 0.0 |
| 164.93669 | 120.317 | -11.476 | 0.0 |
| 242.99057 | 103.45 | -16.493 | 0.0 |
| 248.24118 | 116.026 | -6.973 | 0.0 |
| 304.61342 | 212.051 | -17.131 | 0.0 |
| 228.91057 | 91.879 | -23.314 | 0.0 |
| 308.50567 | 118.188 | -7.159 | 0.0 |
| 419.83955 | 125.007 | -11.017 | 1.0 |
| 189.67465 | 193.129 | -8.674 | 0.0 |
| 242.28526 | 69.129 | -16.121 | 0.0 |
| 213.02812 | 126.919 | -8.439 | 0.0 |
| 290.16771 | 135.96 | -7.305 | 0.0 |
| 143.12444 | 100.706 | -12.15 | 0.0 |
| 278.22975 | 103.227 | -7.35 | 0.0 |
| 260.30975 | 190.015 | -5.148 | 0.0 |
| 112.53506 | 128.054 | -18.969 | 0.0 |
| 248.78975 | 102.113 | -7.149 | 0.0 |
| 178.33751 | 147.659 | -3.494 | 0.0 |
| 242.83383 | 72.278 | -11.75 | 0.0 |
| 226.97751 | 100.012 | -4.935 | 0.0 |
| 172.61669 | 102.747 | -9.067 | 0.0 |
| 188.96934 | 110.208 | -7.915 | 0.0 |
| 129.35791 | 183.983 | -7.223 | 0.0 |
| 169.45587 | 91.872 | -5.507 | 0.0 |
| 57.67791 | 104.328 | -9.213 | 0.0 |
| 294.47791 | 107.29 | -6.285 | 0.0 |
| 372.79302 | 124.018 | -8.312 | 1.0 |
| 226.82077 | 132.001 | -5.636 | 0.0 |
| 171.25832 | 111.129 | -10.311 | 0.0 |
| 130.61179 | 204.936 | -9.571 | 0.0 |
| 123.48036 | 171.624 | -7.069 | 0.0 |
| 230.86975 | 154.048 | -4.541 | 0.0 |
| 181.26322 | 120.07 | -3.623 | 0.0 |
| 102.79138 | 113.374 | -13.804 | 0.0 |
| 385.64526 | 116.331 | -6.748 | 1.0 |
| 193.69751 | 112.542 | -4.375 | 0.0 |
| 249.20771 | 146.99 | -7.306 | 0.0 |
| 172.64281 | 119.993 | -9.333 | 0.0 |
| 152.45016 | 95.981 | -12.144 | 0.0 |
| 142.75873 | 86.66 | -14.115 | 0.0 |
| 413.20444 | 123.564 | -10.417 | 1.0 |
| 208.87465 | 134.331 | -14.099 | 0.0 |
| 200.51546 | 165.825 | -12.445 | 0.0 |
| 337.6322 | 115.649 | -11.181 | 1.0 |
| 222.9024 | 75.527 | -15.198 | 0.0 |
| 221.77914 | 104.983 | -7.908 | 0.0 |
| 262.922 | 156.13 | -13.043 | 0.0 |
| 206.602 | 236.05 | -3.612 | 0.0 |
| 277.9424 | 147.967 | -8.768 | 0.0 |
| 139.4673 | 159.421 | -18.781 | 0.0 |
| 167.73179 | 161.62 | -11.271 | 0.0 |
| 205.71383 | 190.11 | -3.171 | 0.0 |
| 308.55791 | 92.008 | -11.944 | 0.0 |
| 184.89424 | 111.551 | -11.777 | 0.0 |
| 162.42893 | 83.324 | -15.155 | 0.0 |
| 150.04689 | 119.14 | -23.358 | 0.0 |
| 217.15546 | 128.463 | -3.67 | 0.0 |
| 156.49914 | 155.053 | -3.135 | 0.0 |
| 720.74404 | 113.527 | -11.96 | 1.0 |
| 429.84444 | 146.456 | -10.661 | 1.0 |
| 258.61179 | 92.829 | -8.928 | 0.0 |
| 210.31138 | 86.073 | -16.77 | 0.0 |
| 263.02649 | 98.531 | -27.825 | 0.0 |
| 167.49669 | 90.052 | -10.736 | 0.0 |
| 358.08608 | 63.867 | -12.322 | 1.0 |
| 326.19057 | 140.026 | -4.9 | 1.0 |
| 418.42893 | 85.351 | -7.587 | 1.0 |
| 192.13016 | 147.428 | -8.522 | 0.0 |
| 180.61016 | 89.582 | -12.25 | 0.0 |
| 194.21995 | 137.415 | -21.924 | 0.0 |
| 194.5073 | 169.757 | -10.502 | 0.0 |
| 260.64934 | 193.875 | -7.698 | 0.0 |
| 246.38649 | 142.081 | -7.517 | 0.0 |
| 237.40036 | 196.665 | -9.895 | 0.0 |
| 183.71873 | 124.706 | -28.112 | 0.0 |
| 125.64853 | 94.467 | -4.691 | 0.0 |
| 217.0771 | 98.966 | -4.5 | 0.0 |
| 297.22077 | 107.963 | -16.542 | 0.0 |
| 153.57342 | 93.931 | -15.579 | 0.0 |
| 258.35057 | 145.774 | -7.775 | 0.0 |
| 213.13261 | 86.633 | -7.306 | 0.0 |
| 215.66649 | 111.961 | -5.529 | 0.0 |
| 84.4273 | 74.848 | -22.231 | 0.0 |
| 215.87546 | 105.793 | -8.988 | 0.0 |
| 165.48526 | 127.001 | -13.785 | 0.0 |
| 399.33342 | 161.057 | -3.206 | 1.0 |
| 367.93424 | 119.991 | -6.123 | 1.0 |
| 313.44281 | 129.342 | -10.202 | 0.0 |
| 295.67955 | 114.641 | -9.913 | 0.0 |
| 196.5971 | 96.798 | -14.001 | 0.0 |
| 191.4771 | 100.248 | -5.114 | 0.0 |
| 313.44281 | 102.699 | -7.444 | 0.0 |
| 205.11302 | 93.238 | -6.034 | 0.0 |
| 81.42322 | 207.494 | -24.804 | 0.0 |
| 188.78649 | 194.439 | -9.626 | 0.0 |
| 308.24444 | 145.975 | -6.359 | 0.0 |
| 411.92444 | 237.466 | -9.633 | 1.0 |
| 256.86159 | 131.837 | -11.147 | 0.0 |
| 38.45179 | 73.214 | -14.797 | 0.0 |
| 264.77669 | 130.674 | -4.721 | 0.0 |
| 212.4273 | 116.029 | -2.106 | 0.0 |
displayHTML(frameIt("https://en.wikipedia.org/wiki/Euclidean_space",500)) // make sure you run the cell with first wikipedia article!
Do you see the problem in our clusters based on the plot?
As you can see there is very little "separation" (in the sense of being separable into two point clouds, that represent our two identifed clusters, such that they have minimal overlay of these two features, i.e. tempo and loudness. NOTE that this sense of "pairwise separation" is a 2D projection of all three features in 3D Euclidean Space, i.e. loudness, tempo and duration, that depends directly on their two-dimensional visually sense-making projection of perhaps two important song features, as depicted in the corresponding 2D-scatter-plot of tempo versus loudness within the 2D scatter plot matrix that is helping us to partially visualize in the 2D-plane all of the three features in the three dimensional real-valued feature space that was the input to our K-Means algorithm) between loudness, and tempo and generated clusters. To see that, focus on the panels in the first and second columns of the scatter plot matrix. For varying values of loudness and tempo prediction does not change. Instead, duration of a song alone predicts what cluster it belongs to. Why is that?
To see the reason, let's take a look at the marginal distribution of duration in the next cell.
To produce this plot, we have picked histogram from the plots menu and in Plot Options we chose prediction as key and duration as value. The histogram plot shows significant skew in duration. Basically there are a few very long songs. These data points have large leverage on the mean function (what KMeans uses for clustering).
display(transformed.sample(false, fraction = 1.0).select("duration", "prediction")) // plotting over all results
| duration | prediction |
|---|---|
| 213.7073 | 0.0 |
| 133.25016 | 0.0 |
| 247.32689 | 0.0 |
| 337.05751 | 1.0 |
| 430.23628 | 1.0 |
| 186.80118 | 0.0 |
| 361.89995 | 1.0 |
| 220.00281 | 0.0 |
| 156.86485 | 0.0 |
| 256.67873 | 0.0 |
| 204.64281 | 0.0 |
| 112.48281 | 0.0 |
| 170.39628 | 0.0 |
| 215.95383 | 0.0 |
| 303.62077 | 0.0 |
| 266.60526 | 0.0 |
| 326.19057 | 1.0 |
| 51.04281 | 0.0 |
| 129.4624 | 0.0 |
| 253.33506 | 0.0 |
| 237.76608 | 0.0 |
| 132.96281 | 0.0 |
| 399.35955 | 1.0 |
| 168.75057 | 0.0 |
| 396.042 | 1.0 |
| 192.10404 | 0.0 |
| 12.2771 | 0.0 |
| 367.56853 | 1.0 |
| 189.93587 | 0.0 |
| 233.50812 | 0.0 |
| 462.68036 | 1.0 |
| 202.60526 | 0.0 |
| 241.52771 | 0.0 |
| 275.64363 | 0.0 |
| 350.69342 | 1.0 |
| 166.55628 | 0.0 |
| 249.49506 | 0.0 |
| 53.86404 | 0.0 |
| 233.76934 | 0.0 |
| 275.12118 | 0.0 |
| 191.13751 | 0.0 |
| 299.07546 | 0.0 |
| 468.74077 | 1.0 |
| 110.34077 | 0.0 |
| 234.78812 | 0.0 |
| 705.25342 | 1.0 |
| 383.52934 | 1.0 |
| 196.10077 | 0.0 |
| 299.20608 | 0.0 |
| 94.04036 | 0.0 |
| 28.08118 | 0.0 |
| 207.93424 | 0.0 |
| 152.0322 | 0.0 |
| 207.96036 | 0.0 |
| 371.25179 | 1.0 |
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| 399.51628 | 1.0 |
| 271.25506 | 0.0 |
| 172.7473 | 0.0 |
| 231.65342 | 0.0 |
| 208.1171 | 0.0 |
| 195.76118 | 0.0 |
| 723.27791 | 1.0 |
| 282.95791 | 0.0 |
| 153.12934 | 0.0 |
| 207.15057 | 0.0 |
| 174.41914 | 0.0 |
| 269.29587 | 0.0 |
| 275.3824 | 0.0 |
| 149.41995 | 0.0 |
| 108.35546 | 0.0 |
| 243.69587 | 0.0 |
| 308.27057 | 0.0 |
| 204.90404 | 0.0 |
| 311.24853 | 0.0 |
| 164.77995 | 0.0 |
| 449.51465 | 1.0 |
| 140.93016 | 0.0 |
| 165.22404 | 0.0 |
| 53.26322 | 0.0 |
| 218.80118 | 0.0 |
| 300.85179 | 0.0 |
| 388.75383 | 1.0 |
| 150.77832 | 0.0 |
| 293.11955 | 0.0 |
| 177.71057 | 0.0 |
| 184.11057 | 0.0 |
| 225.17506 | 0.0 |
| 272.19546 | 0.0 |
| 157.67465 | 0.0 |
| 204.61669 | 0.0 |
| 93.98812 | 0.0 |
| 204.45995 | 0.0 |
| 307.1473 | 0.0 |
| 347.0624 | 1.0 |
| 184.73751 | 0.0 |
| 146.65098 | 0.0 |
| 513.90649 | 1.0 |
| 293.85098 | 0.0 |
| 121.73016 | 0.0 |
| 86.72608 | 0.0 |
| 171.25832 | 0.0 |
| 264.95955 | 0.0 |
| 411.68934 | 1.0 |
| 190.79791 | 0.0 |
| 159.65995 | 0.0 |
| 162.89914 | 0.0 |
| 205.97506 | 0.0 |
| 204.59057 | 0.0 |
| 117.02812 | 0.0 |
| 135.28771 | 0.0 |
| 163.65669 | 0.0 |
| 254.95465 | 0.0 |
| 178.31138 | 0.0 |
| 150.77832 | 0.0 |
| 410.53995 | 1.0 |
| 222.30159 | 0.0 |
| 314.74893 | 0.0 |
| 233.11628 | 0.0 |
| 226.21995 | 0.0 |
| 441.67791 | 1.0 |
| 120.99873 | 0.0 |
| 157.75302 | 0.0 |
| 203.65016 | 0.0 |
| 287.73832 | 0.0 |
| 226.7424 | 0.0 |
| 69.56363 | 0.0 |
| 174.52363 | 0.0 |
| 363.67628 | 1.0 |
| 136.48934 | 0.0 |
| 390.60853 | 1.0 |
| 284.60363 | 0.0 |
| 291.81342 | 0.0 |
| 502.7522 | 1.0 |
| 197.27628 | 0.0 |
| 329.53424 | 1.0 |
| 340.1922 | 1.0 |
| 170.94485 | 0.0 |
| 113.57995 | 0.0 |
| 205.24363 | 0.0 |
| 169.22077 | 0.0 |
| 285.70077 | 0.0 |
| 221.23057 | 0.0 |
| 310.38649 | 0.0 |
| 353.48853 | 1.0 |
| 415.92118 | 1.0 |
| 150.59546 | 0.0 |
| 236.90404 | 0.0 |
| 227.42159 | 0.0 |
| 229.8771 | 0.0 |
| 359.3922 | 1.0 |
| 403.17342 | 1.0 |
| 296.59383 | 0.0 |
| 117.65506 | 0.0 |
| 241.3971 | 0.0 |
| 34.92526 | 0.0 |
| 188.31628 | 0.0 |
| 409.02485 | 1.0 |
| 335.5424 | 1.0 |
| 354.63791 | 1.0 |
| 213.31546 | 0.0 |
| 238.62812 | 0.0 |
| 193.33179 | 0.0 |
| 225.33179 | 0.0 |
| 166.84363 | 0.0 |
| 79.96036 | 0.0 |
| 158.69342 | 0.0 |
| 176.53506 | 0.0 |
| 347.61098 | 1.0 |
| 106.39628 | 0.0 |
| 147.93098 | 0.0 |
| 446.92853 | 1.0 |
| 360.22812 | 1.0 |
| 214.56934 | 0.0 |
| 325.35465 | 1.0 |
| 413.23057 | 1.0 |
| 218.04363 | 0.0 |
| 215.30077 | 0.0 |
| 57.44281 | 0.0 |
| 247.48363 | 0.0 |
| 793.25995 | 1.0 |
| 467.3824 | 1.0 |
| 327.00036 | 1.0 |
| 232.72444 | 0.0 |
| 251.68934 | 0.0 |
| 197.3024 | 0.0 |
| 193.88036 | 0.0 |
| 383.32036 | 1.0 |
| 269.71383 | 0.0 |
| 255.05914 | 0.0 |
| 337.18812 | 1.0 |
| 240.92689 | 0.0 |
| 206.18404 | 0.0 |
| 143.22893 | 0.0 |
| 244.27057 | 0.0 |
| 83.56526 | 0.0 |
| 428.40771 | 1.0 |
| 261.11955 | 0.0 |
| 208.37832 | 0.0 |
| 369.78893 | 1.0 |
| 47.17669 | 0.0 |
| 239.3073 | 0.0 |
| 17.37098 | 0.0 |
| 257.04444 | 0.0 |
| 198.63465 | 0.0 |
| 208.40444 | 0.0 |
| 338.28526 | 1.0 |
| 175.15057 | 0.0 |
| 234.97098 | 0.0 |
| 275.06893 | 0.0 |
| 186.46159 | 0.0 |
| 201.74322 | 0.0 |
| 237.58322 | 0.0 |
| 219.402 | 0.0 |
| 461.29587 | 1.0 |
| 196.67546 | 0.0 |
| 290.63791 | 0.0 |
| 328.22812 | 1.0 |
| 260.64934 | 0.0 |
| 245.83791 | 0.0 |
| 97.54077 | 0.0 |
| 248.0322 | 0.0 |
| 175.33342 | 0.0 |
| 199.57506 | 0.0 |
| 229.45914 | 0.0 |
| 902.26893 | 1.0 |
| 271.12444 | 0.0 |
| 211.17342 | 0.0 |
| 179.3824 | 0.0 |
| 156.96934 | 0.0 |
| 281.0771 | 0.0 |
| 291.97016 | 0.0 |
| 392.85506 | 1.0 |
| 223.00689 | 0.0 |
| 269.94893 | 0.0 |
| 36.64934 | 0.0 |
| 309.26322 | 0.0 |
| 178.41587 | 0.0 |
| 206.75873 | 0.0 |
| 155.68934 | 0.0 |
| 254.71955 | 0.0 |
| 133.11955 | 0.0 |
| 260.362 | 0.0 |
| 135.28771 | 0.0 |
| 158.27546 | 0.0 |
| 154.93179 | 0.0 |
| 205.84444 | 0.0 |
| 276.21832 | 0.0 |
| 193.61914 | 0.0 |
| 153.73016 | 0.0 |
| 389.11955 | 1.0 |
| 195.23873 | 0.0 |
| 210.72934 | 0.0 |
| 336.06485 | 1.0 |
| 263.02649 | 0.0 |
| 230.26893 | 0.0 |
| 40.6722 | 0.0 |
| 255.92118 | 0.0 |
| 305.60608 | 0.0 |
| 177.8673 | 0.0 |
| 361.11628 | 1.0 |
| 357.66812 | 1.0 |
| 196.49261 | 0.0 |
| 218.40934 | 0.0 |
| 91.58485 | 0.0 |
| 185.25995 | 0.0 |
| 282.80118 | 0.0 |
| 244.68853 | 0.0 |
| 215.40526 | 0.0 |
| 211.19955 | 0.0 |
| 327.75791 | 1.0 |
| 510.40608 | 1.0 |
| 212.74077 | 0.0 |
| 120.86812 | 0.0 |
| 507.08853 | 1.0 |
| 265.11628 | 0.0 |
| 183.06567 | 0.0 |
| 199.54893 | 0.0 |
| 41.92608 | 0.0 |
| 164.75383 | 0.0 |
| 267.33669 | 0.0 |
| 208.74404 | 0.0 |
| 253.09995 | 0.0 |
| 244.50567 | 0.0 |
| 195.73506 | 0.0 |
| 160.07791 | 0.0 |
| 327.70567 | 1.0 |
| 174.86322 | 0.0 |
| 272.92689 | 0.0 |
| 251.53261 | 0.0 |
| 216.99873 | 0.0 |
| 195.3171 | 0.0 |
| 247.11791 | 0.0 |
| 101.3024 | 0.0 |
| 315.97669 | 0.0 |
| 449.67138 | 1.0 |
| 173.16526 | 0.0 |
| 394.44853 | 1.0 |
| 226.69016 | 0.0 |
| 219.11465 | 0.0 |
| 240.92689 | 0.0 |
| 227.91791 | 0.0 |
| 119.84934 | 0.0 |
| 109.92281 | 0.0 |
| 116.08771 | 0.0 |
| 187.71546 | 0.0 |
| 191.65995 | 0.0 |
| 116.32281 | 0.0 |
| 482.45506 | 1.0 |
| 262.71302 | 0.0 |
| 208.97914 | 0.0 |
| 209.81506 | 0.0 |
| 129.85424 | 0.0 |
| 219.0624 | 0.0 |
| 500.4273 | 1.0 |
| 224.7571 | 0.0 |
| 274.85995 | 0.0 |
| 145.162 | 0.0 |
| 211.27791 | 0.0 |
| 167.49669 | 0.0 |
| 415.7122 | 1.0 |
| 346.33098 | 1.0 |
| 399.69914 | 1.0 |
| 136.56771 | 0.0 |
There are known techniques for dealing with skewed features. A simple technique is applying a power transformation. We are going to use the simplest and most common power transformation: logarithm.
In following cell we repeat the clustering experiment with a transformed DataFrame that includes a new column called log_duration.
val df = table("songsTable").selectExpr("tempo", "loudness", "log(duration) as log_duration")
val trainingData2 = new VectorAssembler().
setInputCols(Array("log_duration", "tempo", "loudness")).
setOutputCol("features").
transform(df)
val model2 = new KMeans().setK(2).fit(trainingData2)
val transformed2 = model2.transform(trainingData2).select("log_duration", "tempo", "loudness", "prediction")
display(transformed2.sample(false, fraction = 0.1))
| log_duration | tempo | loudness | prediction |
|---|---|---|---|
| 6.064334546146247 | 121.998 | -6.846 | 1.0 |
| 5.258037100266539 | 123.666 | -12.546 | 1.0 |
| 5.701132566749472 | 110.544 | -8.154 | 1.0 |
| 5.916880511654107 | 83.838 | -6.773 | 1.0 |
| 6.087263380229098 | 108.011 | -7.793 | 1.0 |
| 5.2087027312927825 | 169.892 | -11.187 | 0.0 |
| 5.492162232587351 | 84.753 | -6.803 | 1.0 |
| 5.471616846527706 | 107.832 | -8.638 | 1.0 |
| 5.363261820908567 | 117.974 | -3.807 | 1.0 |
| 6.079997540851444 | 115.995 | -10.967 | 1.0 |
| 5.330035292514153 | 135.26 | -11.146 | 0.0 |
| 5.23255879478462 | 90.885 | -8.762 | 1.0 |
| 5.098409146993903 | 109.509 | -11.688 | 1.0 |
| 4.9194948222974855 | 166.431 | -8.966 | 0.0 |
| 5.255177375047388 | 121.426 | -9.392 | 1.0 |
| 5.003433624211064 | 65.99 | -18.15 | 1.0 |
| 5.147586739998935 | 127.897 | -11.361 | 1.0 |
| 5.4924849397457205 | 116.808 | -8.526 | 1.0 |
| 5.91603579541654 | 107.923 | -13.386 | 1.0 |
| 5.315376921332985 | 172.282 | -11.732 | 0.0 |
| 5.0481981594199565 | 111.826 | -27.174 | 1.0 |
| 5.879897616998539 | 80.011 | -20.412 | 1.0 |
| 5.403094457046498 | 115.084 | -8.535 | 1.0 |
| 5.034688938362198 | 124.039 | -8.9 | 1.0 |
| 4.943992088552383 | 180.034 | -8.643 | 0.0 |
| 5.1808909048116165 | 121.008 | -6.684 | 1.0 |
| 5.863407230069623 | 133.41 | -11.714 | 0.0 |
| 5.404974606095917 | 105.109 | -6.202 | 1.0 |
| 5.88426887636043 | 114.692 | -15.428 | 1.0 |
| 4.493015195631618 | 108.378 | -5.904 | 1.0 |
| 5.752687806796722 | 90.654 | -7.9 | 1.0 |
| 5.347719391111762 | 191.267 | -9.878 | 0.0 |
| 5.343109071331969 | 155.839 | -13.898 | 0.0 |
| 4.394191590641474 | 81.892 | -17.253 | 1.0 |
| 4.879205158949818 | 167.916 | -10.475 | 0.0 |
| 6.019697811017462 | 162.481 | -4.921 | 0.0 |
| 5.451201144193524 | 140.804 | -16.023 | 0.0 |
| 5.212267799598076 | 126.594 | -11.339 | 1.0 |
| 5.060202299303682 | 103.591 | -9.935 | 1.0 |
| 5.029575588906314 | 174.223 | -10.233 | 0.0 |
| 5.2982824659390175 | 43.756 | -16.789 | 1.0 |
| 5.3881634635863005 | 120.944 | -8.862 | 1.0 |
| 5.492592526832023 | 219.306 | -20.349 | 0.0 |
| 5.571559292617606 | 131.909 | -13.378 | 0.0 |
| 6.411462243749755 | 147.183 | -7.165 | 0.0 |
| 5.997550987096423 | 135.003 | -14.34 | 0.0 |
| 6.600945526605945 | 62.98 | -15.22 | 1.0 |
| 5.142410119860136 | 136.089 | -7.688 | 0.0 |
| 6.005052313868685 | 101.182 | -7.541 | 1.0 |
| 5.267510727581979 | 129.956 | -3.927 | 1.0 |
| 5.786199622652282 | 127.09 | -4.866 | 1.0 |
| 5.07370048040891 | 80.762 | -11.391 | 1.0 |
| 5.518391532113626 | 30.832 | -9.864 | 1.0 |
| 5.220913615430304 | 98.901 | -17.031 | 1.0 |
| 6.249435250930559 | 67.841 | -19.533 | 1.0 |
| 5.885358726001259 | 193.1 | -11.081 | 0.0 |
| 4.704283488381446 | 106.153 | -7.04 | 1.0 |
| 5.520380974933744 | 130.191 | -5.42 | 1.0 |
| 5.519229670480138 | 105.352 | -6.023 | 1.0 |
| 5.117526637439253 | 93.552 | -12.528 | 1.0 |
| 5.623553066221829 | 101.951 | -5.001 | 1.0 |
| 5.302843607646548 | 95.944 | -14.027 | 1.0 |
| 5.245310235960805 | 140.057 | -7.697 | 0.0 |
| 5.263191047910029 | 80.464 | -18.496 | 1.0 |
| 5.343982892210335 | 110.041 | -8.578 | 1.0 |
| 5.6999969519310385 | 86.894 | -10.151 | 1.0 |
| 5.2573569249444265 | 132.12 | -20.601 | 0.0 |
| 5.587726858651023 | 89.001 | -9.355 | 1.0 |
| 5.624024726377879 | 88.145 | -6.039 | 1.0 |
| 5.6654048859735715 | 150.069 | -9.229 | 0.0 |
| 5.518391532113626 | 190.664 | -18.539 | 0.0 |
| 5.1808909048116165 | 96.644 | -8.141 | 1.0 |
| 5.5024384933287225 | 162.932 | -9.214 | 0.0 |
| 4.835980274163295 | 132.337 | -8.451 | 0.0 |
| 5.208845565195298 | 98.113 | -5.906 | 1.0 |
| 5.749365533302135 | 126.926 | -10.405 | 1.0 |
| 5.270335397451411 | 89.038 | -12.409 | 1.0 |
| 5.3323103040479065 | 115.136 | -12.186 | 1.0 |
| 5.603059559058559 | 87.611 | -3.653 | 1.0 |
| 4.685417118960068 | 170.459 | -7.971 | 0.0 |
| 5.183531347846387 | 169.186 | -10.352 | 0.0 |
| 5.451537384872765 | 130.075 | -11.559 | 1.0 |
| 5.651097440317911 | 140.057 | -6.712 | 0.0 |
| 6.220097404575559 | 126.003 | -8.606 | 1.0 |
| 5.3241977125281545 | 119.975 | -8.78 | 1.0 |
| 6.0137759121433225 | 167.028 | -6.487 | 0.0 |
| 5.789961271814079 | 154.029 | -6.91 | 0.0 |
| 5.407202667843807 | 68.717 | -18.321 | 1.0 |
| 5.327120737892431 | 210.644 | -18.716 | 0.0 |
| 5.889200011731275 | 150.889 | -15.231 | 0.0 |
| 5.280624822459618 | 42.746 | -7.608 | 1.0 |
| 6.235206644185335 | 151.983 | -7.189 | 0.0 |
| 5.792115857965825 | 103.388 | -15.795 | 1.0 |
| 5.379891500988771 | 84.622 | -12.995 | 1.0 |
| 5.9774886628260795 | 126.988 | -7.598 | 1.0 |
| 5.423584151189881 | 99.976 | -6.921 | 1.0 |
| 4.786235453981626 | 117.532 | -11.684 | 1.0 |
| 4.699778392193864 | 225.791 | -7.064 | 0.0 |
| 5.3462264760360885 | 109.017 | -12.496 | 1.0 |
| 5.616261695215899 | 90.142 | -8.229 | 1.0 |
| 5.153340899640642 | 113.128 | -18.089 | 1.0 |
| 5.439588170019547 | 116.646 | -12.504 | 1.0 |
| 5.7214417936353135 | 57.869 | -26.897 | 1.0 |
| 5.691482505897934 | 86.938 | -4.483 | 1.0 |
| 5.837693420241385 | 131.439 | -12.377 | 0.0 |
| 5.287249998541103 | 96.353 | -6.473 | 1.0 |
| 5.881794174009387 | 90.194 | -11.13 | 1.0 |
| 5.744361367629669 | 83.102 | -9.696 | 1.0 |
| 5.213121558596865 | 85.042 | -5.138 | 1.0 |
| 5.5118720512729995 | 152.964 | -13.695 | 0.0 |
| 5.193444009986724 | 96.01 | -5.837 | 1.0 |
| 5.68004567263334 | 92.659 | -8.59 | 1.0 |
| 5.706965005029758 | 240.448 | -9.875 | 0.0 |
| 4.709709914892611 | 128.862 | -8.669 | 1.0 |
| 5.952001004474009 | 149.83 | -11.42 | 0.0 |
| 5.470188044318821 | 125.025 | -4.917 | 1.0 |
| 5.091365703710136 | 88.133 | -5.638 | 1.0 |
| 6.204281401152069 | 192.504 | -17.651 | 0.0 |
| 5.249433377977233 | 96.484 | -11.025 | 1.0 |
| 5.477202567495158 | 119.973 | -8.448 | 1.0 |
| 5.510816547019034 | 152.932 | -7.368 | 0.0 |
| 5.115646928902234 | 114.75 | -18.492 | 1.0 |
| 5.3942338170785735 | 187.495 | -9.851 | 0.0 |
| 5.633317520186575 | 110.077 | -10.66 | 1.0 |
| 5.489792278278364 | 85.153 | -25.092 | 1.0 |
| 5.118465138450936 | 131.871 | -15.363 | 0.0 |
| 5.260753028847223 | 111.351 | -15.393 | 1.0 |
| 5.750363374801231 | 95.215 | -16.021 | 1.0 |
| 4.812691543803788 | 175.96 | -18.899 | 0.0 |
| 5.426575743730215 | 148.707 | -6.814 | 0.0 |
| 4.857927683654449 | 197.114 | -4.105 | 0.0 |
| 5.460017718602695 | 147.007 | -6.551 | 0.0 |
| 5.199806118788114 | 61.546 | -16.826 | 1.0 |
| 5.641227868701281 | 72.857 | -13.733 | 1.0 |
| 5.579281348076546 | 122.06 | -4.2 | 1.0 |
| 5.812082615379408 | 137.528 | -6.381 | 0.0 |
| 5.362894590061662 | 141.028 | -5.544 | 0.0 |
| 5.170704027647384 | 122.655 | -14.554 | 1.0 |
| 6.038627435601285 | 128.047 | -14.124 | 1.0 |
| 5.045006188946529 | 165.469 | -3.004 | 0.0 |
| 5.109039953782471 | 122.446 | -12.571 | 1.0 |
| 5.54118813055011 | 141.545 | -9.193 | 0.0 |
| 5.32800868428086 | 122.02 | -10.907 | 1.0 |
| 5.667122570497586 | 109.965 | -6.519 | 1.0 |
| 5.0154641497171895 | 63.39 | -13.107 | 1.0 |
| 5.908401094073712 | 113.884 | -6.852 | 1.0 |
| 6.1138473210629245 | 227.001 | -16.889 | 0.0 |
| 5.806205058816614 | 132.383 | -9.016 | 0.0 |
| 5.518496327804453 | 97.069 | -8.376 | 1.0 |
| 6.194307835325854 | 155.568 | -9.598 | 0.0 |
| 5.221195796527836 | 127.304 | -17.207 | 1.0 |
| 5.0053612775551635 | 155.04 | -2.618 | 0.0 |
| 4.883367293037404 | 70.138 | -6.639 | 1.0 |
| 5.452209527247819 | 110.474 | -14.676 | 1.0 |
| 5.134594536181753 | 130.041 | -4.421 | 1.0 |
| 5.652014857268965 | 97.992 | -6.583 | 1.0 |
| 5.393996478288605 | 162.055 | -8.101 | 0.0 |
| 5.6456673680375005 | 112.985 | -5.804 | 1.0 |
| 4.863794002154297 | 99.352 | -11.506 | 1.0 |
| 5.287249998541103 | 158.2 | -8.463 | 0.0 |
| 5.314092039576155 | 131.16 | -15.566 | 0.0 |
| 5.247373958311608 | 95.111 | -32.969 | 1.0 |
| 4.881189259307304 | 90.14 | -11.667 | 1.0 |
| 5.151073471541047 | 102.162 | -18.458 | 1.0 |
| 5.180744021405061 | 127.574 | -7.625 | 1.0 |
| 4.994801464692383 | 77.26 | -16.736 | 1.0 |
| 4.858536086338262 | 166.138 | -7.604 | 0.0 |
| 5.702092447364245 | 143.01 | -6.304 | 0.0 |
| 5.028207592920682 | 100.727 | -11.252 | 1.0 |
| 5.461128195938892 | 89.263 | -9.161 | 1.0 |
| 5.231721530588861 | 90.344 | -14.002 | 1.0 |
| 5.767502911253849 | 140.005 | -6.703 | 0.0 |
| 5.416065734936851 | 97.98 | -5.921 | 1.0 |
| 4.804592602674369 | 91.036 | -14.6 | 1.0 |
| 4.458226274906785 | 95.227 | -18.186 | 1.0 |
| 5.580267040151416 | 116.036 | -8.844 | 1.0 |
| 5.679242859527788 | 125.603 | -5.693 | 1.0 |
| 5.451201144193524 | 119.571 | -9.395 | 1.0 |
| 5.530890439850692 | 150.044 | -8.832 | 0.0 |
| 5.67360501441859 | 81.371 | -7.085 | 1.0 |
| 5.53749315203344 | 144.56 | -5.434 | 0.0 |
| 5.530372729442885 | 96.897 | -7.041 | 1.0 |
| 6.066457379443151 | 135.941 | -11.443 | 0.0 |
| 5.211840646603853 | 74.093 | -18.627 | 1.0 |
| 5.1150195733903905 | 113.24 | -9.424 | 1.0 |
| 5.163107365848958 | 234.853 | -8.071 | 0.0 |
| 4.766868623974747 | 98.38 | -10.965 | 1.0 |
| 5.801557009381438 | 124.859 | -5.928 | 1.0 |
| 5.827973822040402 | 139.917 | -7.787 | 0.0 |
| 5.623741764202893 | 157.485 | -4.855 | 0.0 |
| 5.126717447034353 | 62.246 | -14.606 | 1.0 |
| 5.457236075522337 | 84.991 | -4.545 | 1.0 |
| 5.915754064697231 | 67.495 | -20.449 | 1.0 |
| 5.417689849474705 | 162.07 | -5.124 | 0.0 |
| 5.083463051887064 | 110.856 | -25.629 | 1.0 |
| 5.2364569095267575 | 108.81 | -10.035 | 1.0 |
| 5.504140990115481 | 175.609 | -4.084 | 0.0 |
| 5.782745645993859 | 88.875 | -4.121 | 1.0 |
| 5.428985520263439 | 90.144 | -10.259 | 1.0 |
| 5.394945586446677 | 143.085 | -6.109 | 0.0 |
| 5.446256782063694 | 131.288 | -11.482 | 0.0 |
| 5.038422236258305 | 140.112 | -10.693 | 0.0 |
| 5.2584449210633695 | 150.139 | -7.971 | 0.0 |
| 5.525909607751162 | 125.031 | -5.774 | 1.0 |
| 5.2307437909554215 | 130.193 | -10.108 | 1.0 |
| 5.900421544937317 | 142.991 | -5.235 | 0.0 |
| 4.85589676374095 | 168.071 | -10.85 | 0.0 |
| 3.940312069854874 | 114.519 | -7.18 | 1.0 |
| 6.056899556904485 | 120.939 | -16.839 | 1.0 |
| 5.24199941706601 | 122.322 | -16.949 | 1.0 |
| 5.3677804141515395 | 219.981 | -3.461 | 0.0 |
| 5.5071134027489 | 147.963 | -3.674 | 0.0 |
| 5.365584723524046 | 99.348 | -17.241 | 1.0 |
| 5.277695744967576 | 133.256 | -8.598 | 0.0 |
| 6.0898709287286925 | 125.985 | -10.758 | 1.0 |
| 5.33709625134031 | 124.072 | -13.618 | 1.0 |
| 5.1786851597280235 | 92.191 | -13.569 | 1.0 |
| 5.022028346102827 | 163.439 | -10.039 | 0.0 |
| 5.025637529034568 | 131.458 | -18.005 | 0.0 |
| 6.120757401055048 | 93.972 | -16.714 | 1.0 |
| 4.174267262695061 | 117.909 | -17.923 | 1.0 |
| 6.207917103557972 | 104.98 | -9.259 | 1.0 |
| 6.734419302670816 | 179.835 | -33.438 | 0.0 |
| 6.0230570324407 | 143.619 | -7.186 | 0.0 |
| 4.6061773785992814 | 107.049 | -17.63 | 1.0 |
| 5.229625217696643 | 190.109 | -6.839 | 0.0 |
| 5.511133291041905 | 154.058 | -5.232 | 0.0 |
| 5.446031432894987 | 115.747 | -15.481 | 1.0 |
| 6.615851454127976 | 120.008 | -6.39 | 1.0 |
| 5.481561789037084 | 115.205 | -14.74 | 1.0 |
| 5.734277214176306 | 158.115 | -16.25 | 0.0 |
| 5.140576650244527 | 134.871 | -3.676 | 0.0 |
| 5.88121099672321 | 62.239 | -11.823 | 1.0 |
| 5.53697888891859 | 196.045 | -8.316 | 0.0 |
| 5.775963777688666 | 135.826 | -5.034 | 0.0 |
| 5.417110114495573 | 169.442 | -9.472 | 0.0 |
| 6.102749314680374 | 169.488 | -12.829 | 0.0 |
| 5.2990658363746 | 96.793 | -18.799 | 1.0 |
| 6.199147882794492 | 132.714 | -7.768 | 0.0 |
| 2.48405211811312 | 0.0 | -16.709 | 1.0 |
| 4.637518395185105 | 31.254 | -22.038 | 1.0 |
| 3.254745545095521 | 100.854 | -27.464 | 1.0 |
| 5.3355873802467535 | 123.523 | -7.205 | 1.0 |
| 5.335335656146118 | 50.508 | -10.438 | 1.0 |
| 5.329402430643633 | 128.658 | -12.724 | 1.0 |
| 4.073717615267807 | 70.739 | -16.055 | 1.0 |
| 4.564887525167932 | 238.254 | -11.284 | 0.0 |
| 4.89477353660241 | 180.719 | -13.166 | 0.0 |
| 5.548740736843553 | 90.054 | -10.803 | 1.0 |
| 5.441174600518283 | 80.089 | -11.813 | 1.0 |
| 5.889344691501272 | 205.972 | -8.967 | 0.0 |
| 5.0738640203268615 | 117.048 | -9.627 | 1.0 |
| 5.2087027312927825 | 120.017 | -5.646 | 1.0 |
| 5.4869842077111874 | 135.867 | -8.298 | 0.0 |
| 5.436294020591746 | 84.346 | -14.941 | 1.0 |
| 6.091644939062009 | 168.511 | -1.296 | 0.0 |
| 5.034518929146014 | 80.42 | -12.062 | 1.0 |
| 5.156356142524949 | 74.39 | -14.029 | 1.0 |
| 5.633037175707662 | 134.043 | -5.698 | 0.0 |
| 6.064091650101735 | 125.986 | -10.405 | 1.0 |
| 5.565778351411341 | 129.431 | -4.206 | 1.0 |
| 5.063346206521704 | 130.154 | -14.534 | 1.0 |
| 6.378928383504359 | 113.901 | -20.389 | 1.0 |
| 5.913426759488363 | 85.337 | -10.002 | 1.0 |
| 5.209416751526102 | 125.021 | -6.648 | 1.0 |
| 4.851618460390181 | 115.002 | -18.162 | 1.0 |
| 5.329908761866149 | 44.442 | -10.975 | 1.0 |
| 5.30076107825091 | 93.591 | -9.302 | 1.0 |
| 5.341984382155066 | 159.084 | -11.812 | 0.0 |
| 5.60527203614895 | 111.63 | -6.631 | 1.0 |
| 3.3920520233982674 | 115.901 | -21.162 | 1.0 |
| 5.134132933221371 | 126.245 | -4.765 | 1.0 |
| 4.668397441542593 | 162.139 | -14.93 | 0.0 |
| 5.465447406018871 | 121.993 | -5.775 | 1.0 |
| 5.73038451516079 | 131.11 | -5.061 | 0.0 |
| 5.276762010119172 | 96.441 | -15.351 | 1.0 |
| 5.610351847385838 | 92.758 | -6.482 | 1.0 |
| 5.495599413383852 | 95.377 | -6.289 | 1.0 |
| 5.323815800718434 | 88.023 | -4.729 | 1.0 |
| 5.589973519371366 | 115.341 | -4.88 | 1.0 |
| 5.233534762291271 | 82.92 | -14.814 | 1.0 |
| 5.54853737950824 | 180.153 | -4.271 | 0.0 |
| 4.890462354894079 | 119.575 | -13.016 | 1.0 |
| 6.111880757359681 | 75.202 | -7.952 | 1.0 |
| 6.136076188918148 | 101.863 | -8.621 | 1.0 |
| 5.282484282181109 | 179.942 | -7.084 | 0.0 |
| 5.5081728208599765 | 103.958 | -6.773 | 1.0 |
| 4.910680806169572 | 85.703 | -8.548 | 1.0 |
| 5.5897783399512715 | 105.667 | -8.454 | 1.0 |
| 5.351565768466701 | 130.078 | -8.6 | 1.0 |
| 5.8151253204408455 | 76.75 | -6.823 | 1.0 |
| 5.226963621104301 | 103.26 | -10.707 | 1.0 |
| 5.770928406043289 | 131.749 | -6.099 | 0.0 |
| 5.163107365848958 | 143.446 | -11.994 | 0.0 |
| 4.691665721621109 | 108.718 | -17.471 | 1.0 |
| 6.137318720189338 | 90.672 | -10.488 | 1.0 |
| 4.723496896048981 | 132.015 | -4.98 | 0.0 |
| 5.982575027145929 | 143.82 | -8.604 | 0.0 |
| 5.386849307727097 | 108.89 | -6.368 | 1.0 |
| 3.8280990755466293 | 120.914 | -13.748 | 1.0 |
| 5.47128733320782 | 166.179 | -8.725 | 0.0 |
| 5.167582972957962 | 111.364 | -14.914 | 1.0 |
| 5.475672360360901 | 94.959 | -9.211 | 1.0 |
| 5.140729553205885 | 70.033 | -7.782 | 1.0 |
| 5.775072248464427 | 141.395 | -5.066 | 0.0 |
| 5.453104986343103 | 110.091 | -9.307 | 1.0 |
| 5.099843744640108 | 243.768 | -11.206 | 0.0 |
| 5.4636776829799265 | 129.083 | -3.262 | 1.0 |
| 5.319221592743638 | 100.675 | -13.386 | 1.0 |
| 5.5779984790130435 | 126.625 | -6.831 | 1.0 |
| 5.208274052424202 | 119.453 | -3.818 | 1.0 |
| 5.673963921015516 | 91.982 | -12.072 | 1.0 |
| 5.4936674325315105 | 178.974 | -15.391 | 0.0 |
| 5.9154722545833565 | 124.998 | -9.686 | 1.0 |
| 5.3992054608226665 | 129.961 | -7.922 | 1.0 |
| 5.859465380026135 | 114.669 | -14.172 | 1.0 |
| 6.044899763442047 | 120.309 | -8.014 | 1.0 |
| 5.960726408973159 | 123.838 | -15.306 | 1.0 |
| 5.440155022703225 | 105.76 | -17.516 | 1.0 |
| 5.745113587553698 | 132.025 | -8.882 | 0.0 |
| 4.727665479276123 | 69.439 | -28.0 | 1.0 |
| 5.671089054964057 | 206.088 | -13.704 | 0.0 |
| 5.437658437760656 | 139.042 | -6.488 | 0.0 |
| 2.7231969083654577 | 0.0 | -6.31 | 1.0 |
| 5.73038451516079 | 92.93 | -4.998 | 1.0 |
| 5.116273891085884 | 157.324 | -12.215 | 0.0 |
| 5.328262259295593 | 106.934 | -6.312 | 1.0 |
| 5.230184660726791 | 70.322 | -21.508 | 1.0 |
| 5.482105347616156 | 133.828 | -7.421 | 0.0 |
| 5.486443295335265 | 102.293 | -9.785 | 1.0 |
| 6.341764136593634 | 85.02 | -6.875 | 1.0 |
| 5.502225497673697 | 148.998 | -10.129 | 0.0 |
| 5.827973822040402 | 130.031 | -9.11 | 1.0 |
| 5.484601976728484 | 193.718 | -7.747 | 0.0 |
| 5.527884164573581 | 91.034 | -6.192 | 1.0 |
| 5.172927427877826 | 185.86 | -24.732 | 0.0 |
| 5.2885697626443715 | 127.053 | -5.689 | 1.0 |
| 5.381815742669535 | 130.003 | -6.399 | 1.0 |
| 5.483191579208695 | 107.992 | -13.702 | 1.0 |
| 5.105720054556155 | 187.591 | -13.536 | 0.0 |
| 5.4750158098978945 | 159.666 | -4.36 | 0.0 |
| 5.5472144309132965 | 80.345 | -11.443 | 1.0 |
| 5.56427781084874 | 168.975 | -7.891 | 0.0 |
| 6.1470359886037995 | 82.436 | -12.468 | 1.0 |
| 5.279427623446258 | 84.67 | -5.292 | 1.0 |
| 5.821724993890699 | 83.845 | -13.803 | 1.0 |
| 5.737986555009911 | 133.735 | -10.051 | 0.0 |
| 5.352184762591424 | 116.218 | -7.483 | 1.0 |
| 5.811770016656704 | 110.202 | -9.089 | 1.0 |
| 4.267090277755128 | 107.749 | -3.361 | 1.0 |
| 5.882595494838628 | 82.998 | -16.438 | 1.0 |
| 5.997096507802042 | 237.544 | -14.794 | 0.0 |
| 5.248472811704074 | 126.215 | -15.351 | 1.0 |
| 5.0266663735946135 | 99.977 | -3.827 | 1.0 |
| 5.87550713773137 | 104.225 | -7.841 | 1.0 |
| 4.071492770946719 | 89.232 | -24.717 | 1.0 |
| 5.754758628760087 | 130.036 | -4.706 | 1.0 |
| 5.459350805751235 | 159.978 | -3.232 | 0.0 |
| 5.871023641818345 | 107.863 | -15.638 | 1.0 |
| 5.222746532625901 | 131.978 | -5.67 | 0.0 |
| 6.341626102410307 | 123.993 | -8.585 | 1.0 |
| 4.786453370526792 | 130.029 | -9.234 | 1.0 |
| 5.658686742660347 | 125.107 | -11.981 | 1.0 |
| 4.780333181311705 | 181.15 | -7.41 | 0.0 |
| 5.453664230661525 | 130.128 | -4.531 | 1.0 |
| 5.143783004790946 | 93.053 | -10.299 | 1.0 |
| 5.569768756207467 | 165.932 | -6.124 | 0.0 |
| 5.124544227693321 | 146.426 | -10.195 | 0.0 |
| 6.000661797437479 | 129.007 | -7.181 | 1.0 |
| 5.517867348720116 | 191.787 | -12.92 | 0.0 |
| 4.370691635610005 | 89.638 | -6.059 | 1.0 |
| 5.525285235827343 | 156.019 | -5.927 | 0.0 |
| 5.755337701403143 | 131.966 | -7.267 | 0.0 |
| 5.53687601242964 | 130.494 | -4.506 | 1.0 |
| 5.271677659599143 | 112.984 | -5.779 | 1.0 |
| 5.094413293795442 | 45.955 | -12.685 | 1.0 |
| 5.41745801356627 | 108.017 | -6.207 | 1.0 |
| 5.398023958861762 | 125.085 | -12.094 | 1.0 |
| 5.094893657240654 | 62.415 | -16.983 | 1.0 |
| 5.130586525664302 | 116.063 | -10.601 | 1.0 |
| 5.22822526613027 | 110.102 | -16.477 | 1.0 |
| 5.286589462747781 | 96.469 | -7.544 | 1.0 |
| 5.521635422635403 | 75.186 | -5.503 | 1.0 |
| 5.025980616731968 | 114.348 | -2.548 | 1.0 |
| 5.51943911539636 | 94.183 | -5.847 | 1.0 |
| 5.697458956625815 | 99.988 | -10.737 | 1.0 |
| 5.86503890587163 | 91.139 | -9.13 | 1.0 |
| 5.489360777153861 | 100.992 | -3.3 | 1.0 |
| 5.3578617345120865 | 121.875 | -6.827 | 1.0 |
| 5.769217125399671 | 72.299 | -14.883 | 1.0 |
| 5.046015307863599 | 109.808 | -12.783 | 1.0 |
| 5.274223066183203 | 111.052 | -9.008 | 1.0 |
| 5.9798699133794955 | 131.997 | -3.879 | 0.0 |
| 5.580858019525172 | 128.0 | -4.988 | 1.0 |
| 5.82928035569681 | 97.345 | -11.499 | 1.0 |
| 6.094712435238956 | 126.965 | -7.269 | 1.0 |
| 5.679867340584397 | 140.047 | -5.647 | 0.0 |
| 5.404504900132244 | 152.579 | -12.128 | 0.0 |
| 5.726391454576292 | 172.19 | -10.391 | 0.0 |
| 6.26857580737286 | 127.984 | -8.235 | 1.0 |
| 5.527987970235486 | 159.966 | -13.347 | 0.0 |
| 5.215820203328058 | 121.171 | -9.677 | 1.0 |
| 5.172038638226674 | 171.333 | -12.577 | 0.0 |
| 5.114391824056528 | 118.825 | -17.069 | 1.0 |
| 5.443210556309651 | 90.877 | -20.028 | 1.0 |
| 6.341764136593634 | 125.005 | -11.208 | 1.0 |
| 5.410010026795568 | 104.63 | -5.123 | 1.0 |
| 5.157258916570002 | 94.011 | -11.14 | 1.0 |
| 5.3335720040567285 | 197.822 | -8.291 | 0.0 |
| 5.722467876781503 | 133.342 | -15.668 | 0.0 |
| 4.906051143655614 | 150.871 | -12.438 | 0.0 |
| 5.639279330670629 | 128.078 | -5.433 | 1.0 |
| 3.9398041119997855 | 74.721 | -22.394 | 1.0 |
| 5.784433950838736 | 95.991 | -16.756 | 1.0 |
| 5.419195651418814 | 119.999 | -3.964 | 1.0 |
| 5.365584723524046 | 105.987 | -3.815 | 1.0 |
| 5.224294867664447 | 184.893 | -5.982 | 0.0 |
| 5.216670936098296 | 202.14 | -4.63 | 0.0 |
| 5.503715637575613 | 117.547 | -11.625 | 1.0 |
| 5.423814571344231 | 144.818 | -8.575 | 0.0 |
| 5.573346590758997 | 81.476 | -15.363 | 1.0 |
| 5.3717933140162275 | 151.128 | -6.697 | 0.0 |
| 7.0423371496613365 | 77.561 | -15.204 | 1.0 |
| 5.65027103779804 | 114.308 | -6.593 | 1.0 |
| 6.1900333988798035 | 114.924 | -10.508 | 1.0 |
| 5.15981242631456 | 112.042 | -9.297 | 1.0 |
| 5.367292904551431 | 135.027 | -7.629 | 0.0 |
| 5.212125453705929 | 126.01 | -2.728 | 1.0 |
| 5.801951745180824 | 104.385 | -22.224 | 1.0 |
| 3.8993628021851645 | 56.245 | -10.257 | 1.0 |
| 6.025332353214288 | 135.958 | -8.944 | 0.0 |
| 4.711120737540498 | 135.419 | -23.929 | 0.0 |
| 5.3176854876953845 | 113.557 | -7.572 | 1.0 |
| 4.88257583642476 | 104.714 | -9.823 | 1.0 |
| 5.288042055894653 | 142.744 | -8.189 | 0.0 |
| 5.726817030503055 | 80.231 | -13.916 | 1.0 |
| 5.568373930809033 | 105.635 | -11.253 | 1.0 |
| 5.574139929699739 | 94.151 | -10.945 | 1.0 |
| 5.314220636516537 | 116.358 | -10.622 | 1.0 |
| 5.185872594474605 | 81.181 | -7.193 | 1.0 |
| 5.263191047910029 | 92.266 | -15.062 | 1.0 |
| 5.5339908888778595 | 108.004 | -9.795 | 1.0 |
| 5.056715949590252 | 165.439 | -9.06 | 0.0 |
| 3.9408197698197713 | 122.261 | -18.636 | 1.0 |
| 6.203859013449871 | 197.914 | -11.021 | 0.0 |
| 5.865335290950203 | 132.092 | -12.164 | 0.0 |
| 5.2981518028431225 | 84.141 | -4.425 | 1.0 |
| 5.317301129254338 | 93.069 | -10.056 | 1.0 |
| 5.531924978120003 | 140.965 | -10.094 | 0.0 |
| 5.374096459458477 | 151.936 | -6.81 | 0.0 |
| 5.945936444126941 | 134.409 | -7.102 | 0.0 |
| 7.211785910002847 | 96.83 | -18.114 | 1.0 |
| 5.3500785889402005 | 147.991 | -11.104 | 0.0 |
| 5.027522892507361 | 120.527 | -7.075 | 1.0 |
| 5.465115810845681 | 124.005 | -7.957 | 1.0 |
| 4.956938481993811 | 119.446 | -19.653 | 1.0 |
| 4.6649585052598415 | 163.919 | -4.968 | 0.0 |
| 5.528403124883772 | 104.907 | -13.77 | 1.0 |
| 5.469527924173878 | 103.983 | -8.127 | 1.0 |
| 5.648984132478454 | 163.031 | -4.385 | 0.0 |
| 5.45679029199923 | 155.982 | -6.263 | 0.0 |
| 5.030941716035228 | 111.224 | -3.903 | 1.0 |
| 5.270066697826635 | 140.063 | -4.937 | 0.0 |
| 5.217520891529926 | 94.307 | -8.907 | 1.0 |
| 5.622514716915152 | 101.809 | -11.666 | 1.0 |
| 6.5302317923969815 | 123.571 | -12.814 | 1.0 |
| 4.828068427216242 | 231.953 | -8.168 | 0.0 |
| 6.153610203197906 | 121.026 | -10.18 | 1.0 |
| 5.584003924284049 | 100.083 | -7.944 | 1.0 |
| 5.2237321189780594 | 160.038 | -10.534 | 0.0 |
| 5.494204452408169 | 105.99 | -5.787 | 1.0 |
| 5.219218692301358 | 94.268 | -7.142 | 1.0 |
| 5.515031936175296 | 87.356 | -10.521 | 1.0 |
| 4.612680214992582 | 152.43 | -12.941 | 0.0 |
| 5.173519496873623 | 136.255 | -9.675 | 0.0 |
| 4.976229408368982 | 143.87 | -15.836 | 0.0 |
| 5.61910883575362 | 143.004 | -5.542 | 0.0 |
| 5.925916423159631 | 120.003 | -12.754 | 1.0 |
| 5.413158924742671 | 93.787 | -9.833 | 1.0 |
| 4.731816845633907 | 185.055 | -13.618 | 0.0 |
| 5.286721594699027 | 84.843 | -6.906 | 1.0 |
| 5.995536744446201 | 90.218 | -16.787 | 1.0 |
| 5.5118720512729995 | 154.237 | -7.34 | 0.0 |
| 5.339229916967983 | 98.959 | -3.163 | 1.0 |
| 5.303493497240193 | 135.886 | -6.352 | 0.0 |
| 5.449181408435859 | 95.017 | -4.009 | 1.0 |
| 5.344731328832199 | 152.703 | -11.977 | 0.0 |
| 4.904696833682751 | 104.281 | -13.585 | 1.0 |
| 5.809422421269561 | 210.415 | -10.595 | 0.0 |
| 5.136746105517679 | 132.335 | -4.782 | 0.0 |
| 5.186310982615107 | 105.05 | -4.581 | 1.0 |
| 5.002556212696598 | 162.564 | -7.579 | 0.0 |
| 5.408841273925989 | 119.996 | -5.901 | 1.0 |
| 5.381335029266696 | 103.008 | -6.436 | 1.0 |
| 5.460906216101348 | 138.07 | -14.387 | 0.0 |
| 4.42843730342153 | 114.345 | -4.951 | 1.0 |
| 5.614263884505508 | 163.274 | -4.265 | 0.0 |
| 5.226261971032257 | 103.479 | -16.027 | 1.0 |
| 5.307125163770039 | 107.578 | -16.041 | 1.0 |
| 4.8756237208303785 | 147.871 | -5.761 | 0.0 |
| 5.93687353173708 | 90.628 | -11.245 | 1.0 |
| 5.370335935990799 | 231.051 | -12.612 | 0.0 |
| 5.871170975191018 | 180.042 | -10.383 | 0.0 |
| 5.599973125687407 | 78.979 | -6.21 | 1.0 |
| 4.67280173129088 | 109.925 | -14.526 | 1.0 |
| 5.568971940469297 | 91.041 | -6.672 | 1.0 |
| 5.603540945712016 | 120.025 | -11.02 | 1.0 |
| 5.400975032797074 | 205.998 | -6.543 | 0.0 |
| 5.245034726302448 | 113.111 | -8.918 | 1.0 |
| 5.372521184108441 | 112.056 | -13.859 | 1.0 |
| 5.704096558659989 | 119.673 | -14.087 | 1.0 |
| 5.479275577741912 | 119.922 | -8.373 | 1.0 |
| 5.865113031652334 | 86.034 | -16.263 | 1.0 |
| 5.032135499299219 | 67.825 | -9.567 | 1.0 |
| 6.245339882354565 | 94.557 | -7.916 | 1.0 |
| 5.85834692601107 | 105.079 | -6.254 | 1.0 |
| 5.9040629940095535 | 92.679 | -9.476 | 1.0 |
| 5.600456000044702 | 167.925 | -3.526 | 0.0 |
| 4.38901689577076 | 126.156 | -7.754 | 1.0 |
| 5.274223066183203 | 170.027 | -4.813 | 0.0 |
| 5.249296225705547 | 165.012 | -5.835 | 0.0 |
| 5.507219386966034 | 167.92 | -5.221 | 0.0 |
| 5.600842139109561 | 191.836 | -8.413 | 0.0 |
| 5.9780183254931805 | 116.221 | -10.097 | 1.0 |
| 5.629291839552761 | 88.028 | -5.63 | 1.0 |
| 5.45556335097355 | 133.997 | -4.746 | 0.0 |
| 5.453328704474266 | 99.637 | -12.155 | 1.0 |
| 5.645759675578243 | 219.732 | -7.422 | 0.0 |
| 4.7148731097299965 | 175.881 | -3.677 | 0.0 |
| 5.443436498740101 | 147.246 | -7.101 | 0.0 |
| 6.264707329199452 | 96.399 | -12.444 | 1.0 |
| 5.555024969924673 | 49.83 | -5.13 | 1.0 |
| 5.249707678591793 | 139.951 | -13.486 | 0.0 |
| 5.683161652730066 | 70.441 | -14.599 | 1.0 |
| 5.602288843325435 | 103.795 | -11.676 | 1.0 |
| 4.510971148636285 | 179.085 | -8.22 | 0.0 |
| 5.419427084876769 | 159.842 | -14.056 | 0.0 |
| 5.7238343519327755 | 117.917 | -11.966 | 1.0 |
| 5.149407413424839 | 116.302 | -6.262 | 1.0 |
| 5.358353862511185 | 53.788 | -10.868 | 1.0 |
| 5.049874019907821 | 197.29 | -23.707 | 0.0 |
| 4.589865463932764 | 158.84 | -7.684 | 0.0 |
| 5.364240537989767 | 100.075 | -4.86 | 1.0 |
| 5.106827932903924 | 150.064 | -14.802 | 0.0 |
| 5.384934736760523 | 105.136 | -3.783 | 1.0 |
| 5.505097362174365 | 127.985 | -4.444 | 1.0 |
| 5.143478108038215 | 87.533 | -7.045 | 1.0 |
| 4.2601062865062 | 74.36 | -12.725 | 1.0 |
| 5.199085200801644 | 147.04 | -8.922 | 0.0 |
| 5.584592662453535 | 99.268 | -12.358 | 1.0 |
| 5.679421302966559 | 81.362 | -7.025 | 1.0 |
| 5.333824124190991 | 79.702 | -17.71 | 1.0 |
| 5.331931526930796 | 136.835 | -12.374 | 0.0 |
| 5.375427470647804 | 141.721 | -13.633 | 0.0 |
| 5.337221875868001 | 146.362 | -8.55 | 0.0 |
| 5.653572558115166 | 106.373 | -7.068 | 1.0 |
| 5.384934736760523 | 163.979 | -1.952 | 0.0 |
| 5.226683009407633 | 217.213 | -17.298 | 0.0 |
| 5.061858242676265 | 89.004 | -13.367 | 1.0 |
| 5.417689849474705 | 143.676 | -6.858 | 0.0 |
| 5.356630330305323 | 125.111 | -8.183 | 1.0 |
| 5.162060147947414 | 103.718 | -15.488 | 1.0 |
| 6.218537428483649 | 125.008 | -7.708 | 1.0 |
| 5.101435379302713 | 125.069 | -9.807 | 1.0 |
| 5.48784907594078 | 104.492 | -7.694 | 1.0 |
| 5.78250424874252 | 130.039 | -5.188 | 1.0 |
| 5.088147664703838 | 183.836 | -4.671 | 0.0 |
| 6.3148595771487095 | 120.373 | -8.901 | 1.0 |
| 5.451537384872765 | 168.477 | -7.374 | 0.0 |
| 5.706183523164159 | 99.927 | -9.566 | 1.0 |
| 5.502332001172103 | 95.333 | -6.493 | 1.0 |
| 5.521739878948984 | 169.369 | -12.558 | 0.0 |
| 4.711120737540498 | 138.238 | -8.843 | 0.0 |
| 5.609491062398614 | 87.041 | -6.835 | 1.0 |
| 5.180009168017768 | 126.865 | -6.458 | 1.0 |
| 5.891223432702917 | 207.899 | -10.47 | 0.0 |
| 5.5207992990407755 | 67.511 | -8.083 | 1.0 |
| 5.890428998615078 | 0.0 | -10.695 | 1.0 |
| 5.883323391273292 | 73.209 | -20.003 | 1.0 |
| 3.584866086200537 | 97.91 | -13.766 | 1.0 |
| 5.08927517211037 | 86.478 | -6.99 | 1.0 |
| 5.812551315369655 | 179.721 | -14.285 | 0.0 |
| 5.458127046842273 | 110.03 | -5.795 | 1.0 |
| 5.648616145884805 | 94.383 | -7.25 | 1.0 |
| 5.425196110546038 | 167.907 | -8.459 | 0.0 |
| 5.219642706004484 | 62.586 | -20.636 | 1.0 |
| 6.295629588470401 | 126.038 | -12.04 | 1.0 |
| 5.711554758718933 | 132.723 | -7.209 | 0.0 |
| 5.389952711501166 | 127.897 | -6.24 | 1.0 |
| 5.026323521131626 | 149.006 | -5.661 | 0.0 |
| 6.186009468267858 | 131.985 | -4.922 | 0.0 |
| 5.7577331101832545 | 125.008 | -7.131 | 1.0 |
| 4.9099106826762755 | 79.832 | -11.845 | 1.0 |
| 5.731570843290727 | 142.118 | -5.631 | 0.0 |
| 5.1643027714857315 | 96.796 | -8.626 | 1.0 |
| 5.063676599821411 | 207.965 | -5.222 | 0.0 |
| 5.143325566411583 | 105.504 | -7.655 | 1.0 |
| 5.201965759646714 | 96.005 | -4.967 | 1.0 |
| 5.519648476382033 | 98.964 | -7.278 | 1.0 |
| 5.030600326504665 | 79.426 | -10.867 | 1.0 |
| 5.140576650244527 | 165.52 | -14.734 | 0.0 |
| 5.311001679436338 | 89.441 | -6.668 | 1.0 |
| 5.634624696715785 | 133.31 | -4.637 | 0.0 |
| 5.364729537556912 | 160.023 | -10.053 | 0.0 |
| 5.8619215616314975 | 175.06 | -7.078 | 0.0 |
| 5.452433445776701 | 114.964 | -7.285 | 1.0 |
| 4.986450997291523 | 93.5 | -3.815 | 1.0 |
| 5.488929089755756 | 101.06 | -7.238 | 1.0 |
| 5.139964745919678 | 84.774 | -9.253 | 1.0 |
| 5.363139425609932 | 101.752 | -12.863 | 1.0 |
| 5.330288354217008 | 95.627 | -15.966 | 1.0 |
| 5.229625217696643 | 179.952 | -13.267 | 0.0 |
| 5.29278142144123 | 151.535 | -5.267 | 0.0 |
| 5.809892369459324 | 132.981 | -10.682 | 0.0 |
| 5.849504590869244 | 220.039 | -5.11 | 0.0 |
| 5.519962475758416 | 160.106 | -5.807 | 0.0 |
| 5.023232876959716 | 80.384 | -14.562 | 1.0 |
| 5.827512296778853 | 142.155 | -6.301 | 0.0 |
| 4.476217893264733 | 176.354 | -5.243 | 0.0 |
| 4.716977625501516 | 121.601 | -11.847 | 1.0 |
| 4.187047102663966 | 87.83 | -13.689 | 1.0 |
| 5.377842940530993 | 119.762 | -4.769 | 1.0 |
| 4.952703169254423 | 110.686 | -11.218 | 1.0 |
| 5.529440238242905 | 90.929 | -14.502 | 1.0 |
| 5.363261820908567 | 122.936 | -9.6 | 1.0 |
| 5.156205552886701 | 62.968 | -8.561 | 1.0 |
| 4.929365873206521 | 102.929 | -12.912 | 1.0 |
| 5.105720054556155 | 86.232 | -7.316 | 1.0 |
| 5.551786455213501 | 128.896 | -15.767 | 1.0 |
| 5.492700061177452 | 98.054 | -6.596 | 1.0 |
| 4.682278160773387 | 127.932 | -10.676 | 1.0 |
| 5.910244404863671 | 111.747 | -7.658 | 1.0 |
| 5.308419022584635 | 92.062 | -7.396 | 1.0 |
| 4.966084411461676 | 135.534 | -10.839 | 0.0 |
| 4.973883477375123 | 115.076 | -25.03 | 1.0 |
| 6.175525132581313 | 202.045 | -11.959 | 0.0 |
| 5.113449430480435 | 112.603 | -16.584 | 1.0 |
| 5.288305969330356 | 112.565 | -11.702 | 1.0 |
| 5.983102003303539 | 159.789 | -11.868 | 0.0 |
| 5.567176799358838 | 145.129 | -4.733 | 0.0 |
| 5.58095646957768 | 104.256 | -15.552 | 1.0 |
| 5.478948537110532 | 105.101 | -9.257 | 1.0 |
| 5.641598583644489 | 97.622 | -13.01 | 1.0 |
| 5.822421232966385 | 162.531 | -13.611 | 0.0 |
| 5.353668815476439 | 71.494 | -11.247 | 1.0 |
| 5.275159224804866 | 127.005 | -5.155 | 1.0 |
| 5.217095977001863 | 203.536 | -5.012 | 0.0 |
| 5.248884603456135 | 138.029 | -4.596 | 0.0 |
| 5.428412298800484 | 101.978 | -5.839 | 1.0 |
| 6.1580428262859765 | 97.999 | -10.201 | 1.0 |
| 5.700346511824177 | 100.664 | -8.013 | 1.0 |
| 5.376998178031043 | 132.682 | -12.438 | 0.0 |
| 5.494741225132456 | 131.49 | -11.232 | 0.0 |
| 5.732586590899394 | 201.81 | -4.167 | 0.0 |
| 5.547723458446129 | 140.081 | -7.26 | 0.0 |
| 5.090562179678058 | 163.105 | -6.223 | 0.0 |
| 5.362649647669598 | 110.489 | -6.202 | 1.0 |
| 5.377239631375239 | 106.01 | -5.466 | 1.0 |
| 5.5502647555781275 | 85.61 | -5.781 | 1.0 |
| 5.362527177416307 | 135.122 | -8.42 | 0.0 |
| 5.102706841247405 | 122.77 | -7.029 | 1.0 |
| 5.680402275474472 | 118.03 | -5.798 | 1.0 |
| 5.546908874376848 | 189.986 | -4.537 | 0.0 |
| 5.5721554009981205 | 110.946 | -12.825 | 1.0 |
| 5.2564039954429544 | 99.836 | -15.637 | 1.0 |
| 5.215536501039555 | 87.731 | -17.643 | 1.0 |
| 6.448692317766558 | 124.792 | -10.583 | 1.0 |
| 5.463899048527427 | 142.86 | -5.746 | 0.0 |
| 2.148000485700547 | 0.0 | -8.435 | 1.0 |
| 6.320232899153641 | 126.024 | -12.36 | 1.0 |
| 5.606040456996324 | 147.692 | -7.499 | 0.0 |
| 5.400975032797074 | 162.898 | -2.856 | 0.0 |
| 5.239509116005083 | 134.153 | -17.027 | 0.0 |
| 5.577405806789707 | 115.884 | -5.74 | 1.0 |
| 4.549264269798323 | 121.576 | -21.839 | 1.0 |
| 3.6527962139408547 | 41.35 | -23.844 | 1.0 |
| 5.214968800541323 | 111.461 | -9.788 | 1.0 |
| 5.28513470584072 | 172.75 | -10.357 | 0.0 |
| 5.19460377174902 | 171.625 | -17.449 | 0.0 |
| 4.398055114513321 | 96.895 | -6.086 | 1.0 |
| 5.471507020818305 | 159.737 | -7.027 | 0.0 |
| 4.83078212831702 | 133.981 | -15.796 | 0.0 |
| 5.481779231277037 | 132.01 | -2.852 | 0.0 |
| 5.813175927081176 | 82.216 | -15.964 | 1.0 |
| 5.441401003374007 | 87.026 | -5.83 | 1.0 |
| 5.325596776831915 | 198.851 | -4.724 | 0.0 |
| 5.427609209970511 | 85.625 | -17.708 | 1.0 |
| 5.474796891858358 | 175.59 | -5.836 | 0.0 |
| 5.5875312401931 | 116.656 | -6.006 | 1.0 |
| 6.291380825532051 | 124.024 | -7.941 | 1.0 |
| 4.753670705106405 | 163.511 | -5.599 | 0.0 |
| 5.235900965453425 | 89.44 | -11.965 | 1.0 |
| 5.52194879882897 | 120.01 | -4.914 | 1.0 |
| 6.214261018196818 | 126.976 | -5.224 | 1.0 |
| 5.265218234227128 | 131.675 | -6.413 | 0.0 |
| 5.485902090214787 | 91.996 | -2.446 | 1.0 |
| 5.955595086230608 | 126.687 | -10.763 | 1.0 |
| 5.605368148637933 | 120.034 | -8.096 | 1.0 |
| 5.7297908066738845 | 120.002 | -11.244 | 1.0 |
| 6.8447011063207555 | 115.593 | -12.89 | 1.0 |
| 5.059705038227187 | 97.891 | -10.768 | 1.0 |
| 5.641876503095035 | 139.593 | -9.691 | 0.0 |
| 5.070753058070189 | 114.43 | -12.898 | 1.0 |
| 5.264002384594248 | 140.596 | -10.308 | 0.0 |
| 7.319039310933789 | 60.94 | -21.661 | 1.0 |
| 5.242413882601138 | 56.142 | -17.18 | 1.0 |
| 5.871465548643589 | 76.905 | -7.595 | 1.0 |
| 5.3323103040479065 | 185.577 | -5.884 | 0.0 |
| 5.462237483923419 | 125.022 | -5.941 | 1.0 |
| 5.562775015280007 | 103.544 | -15.525 | 1.0 |
| 5.976096943041082 | 165.33 | -7.812 | 0.0 |
| 5.054384966781395 | 81.619 | -16.783 | 1.0 |
| 5.509336918302029 | 172.147 | -16.273 | 0.0 |
| 5.243656196703 | 154.542 | -5.113 | 0.0 |
| 5.208274052424202 | 163.974 | -6.227 | 0.0 |
| 5.528403124883772 | 93.04 | -5.971 | 1.0 |
| 5.607000171810274 | 111.365 | -10.116 | 1.0 |
| 5.237845445572235 | 95.722 | -10.6 | 1.0 |
| 4.898481981767724 | 96.288 | -5.465 | 1.0 |
| 5.10714420967513 | 131.144 | -10.51 | 0.0 |
| 5.731740222431003 | 103.682 | -12.932 | 1.0 |
| 6.153832302679555 | 139.983 | -5.243 | 0.0 |
| 6.140755810501547 | 127.983 | -9.151 | 1.0 |
| 5.816526554590968 | 89.275 | -3.902 | 1.0 |
| 5.466772646002193 | 124.169 | -4.783 | 1.0 |
| 5.532235146841884 | 160.071 | -10.728 | 0.0 |
| 4.714170589210512 | 216.105 | -5.759 | 0.0 |
| 5.26710653645963 | 91.057 | -16.497 | 1.0 |
| 5.344357180540937 | 189.847 | -5.161 | 0.0 |
| 4.946409115285852 | 78.587 | -16.969 | 1.0 |
| 5.6151206090866115 | 113.798 | -23.995 | 1.0 |
| 5.472714566733009 | 119.944 | -4.863 | 1.0 |
| 2.416456488150271 | 85.918 | -29.744 | 1.0 |
| 5.95281586631299 | 137.976 | -7.193 | 0.0 |
| 5.224997845023872 | 101.607 | -15.219 | 1.0 |
| 5.741933673775497 | 89.962 | -21.141 | 1.0 |
| 5.032987413771107 | 149.967 | -17.245 | 0.0 |
| 5.156657138568294 | 125.669 | -9.773 | 1.0 |
| 5.096173397462181 | 116.699 | -16.736 | 1.0 |
| 5.941827117860842 | 129.44 | -8.481 | 1.0 |
| 5.75326807949039 | 100.08 | -6.938 | 1.0 |
| 5.239370542691106 | 160.193 | -12.146 | 0.0 |
| 5.487308631306926 | 93.941 | -8.156 | 1.0 |
| 5.497527627913457 | 102.915 | -7.999 | 1.0 |
| 4.687343913732275 | 96.17 | -3.083 | 1.0 |
| 5.740927380966297 | 96.192 | -18.309 | 1.0 |
| 5.027865268551932 | 113.231 | -18.821 | 1.0 |
| 5.057214762509251 | 193.52 | -7.094 | 0.0 |
| 5.043490663158142 | 120.9 | -11.805 | 1.0 |
| 5.545175686665518 | 177.047 | -7.456 | 0.0 |
| 5.4739206562390805 | 102.869 | -5.441 | 1.0 |
| 3.7525906018224884 | 114.055 | -3.771 | 1.0 |
| 5.250256001739822 | 98.65 | -6.818 | 1.0 |
| 5.488929089755756 | 125.022 | -9.259 | 1.0 |
| 6.2047563691077325 | 65.068 | -11.986 | 1.0 |
| 5.564978348428669 | 133.972 | -5.126 | 0.0 |
| 5.766522034883475 | 196.016 | -6.212 | 0.0 |
| 5.715607384022325 | 135.248 | -7.133 | 0.0 |
| 5.988618551100562 | 126.002 | -9.284 | 1.0 |
| 5.2432422458510715 | 112.895 | -5.149 | 1.0 |
| 5.98092644079866 | 135.062 | -5.471 | 0.0 |
| 5.96971012630147 | 85.231 | -11.807 | 1.0 |
| 5.695529310800033 | 125.142 | -6.341 | 1.0 |
| 5.574437279097848 | 127.022 | -10.96 | 1.0 |
| 5.736639295234792 | 94.001 | -8.066 | 1.0 |
| 5.252856399447034 | 138.201 | -4.53 | 0.0 |
| 4.842800613938594 | 148.436 | -4.54 | 0.0 |
| 5.326993837902569 | 124.389 | -7.918 | 1.0 |
| 5.309323674314746 | 135.08 | -7.997 | 0.0 |
| 5.309452885876903 | 212.408 | -14.007 | 0.0 |
| 5.237151391976638 | 114.878 | -8.252 | 1.0 |
| 5.638536076366039 | 151.461 | -12.952 | 0.0 |
| 5.488173219119607 | 171.439 | -13.77 | 0.0 |
| 5.327501389830855 | 91.117 | -6.481 | 1.0 |
| 5.296713831572754 | 100.017 | -9.108 | 1.0 |
| 5.944979097542342 | 113.891 | -19.76 | 1.0 |
| 5.990385289919405 | 92.99 | -5.934 | 1.0 |
| 5.396604338579462 | 93.991 | -5.766 | 1.0 |
| 4.864197272882948 | 182.695 | -14.1 | 0.0 |
| 5.262243612520556 | 138.059 | -7.885 | 0.0 |
| 5.664771339539909 | 87.545 | -9.949 | 1.0 |
| 5.351070287662016 | 69.291 | -19.93 | 1.0 |
| 6.131885446236486 | 164.968 | -7.435 | 0.0 |
| 5.186164893232419 | 43.871 | -19.915 | 1.0 |
| 5.440834856737833 | 150.118 | -4.63 | 0.0 |
| 5.385174281609037 | 99.982 | -6.635 | 1.0 |
| 6.019824791560191 | 129.05 | -5.429 | 1.0 |
| 5.502970824816003 | 129.989 | -4.38 | 1.0 |
| 5.462902432614862 | 180.366 | -3.918 | 0.0 |
| 5.508066937648978 | 123.614 | -9.07 | 1.0 |
| 5.512821061562089 | 84.017 | -6.863 | 1.0 |
| 4.63168445542683 | 102.168 | -24.321 | 1.0 |
| 6.125851723445143 | 109.562 | -10.705 | 1.0 |
| 5.507219386966034 | 161.141 | -15.025 | 0.0 |
| 5.64815598094485 | 173.833 | -5.775 | 0.0 |
| 5.086211858768461 | 186.861 | -3.525 | 0.0 |
| 5.902993350554627 | 127.192 | -9.154 | 1.0 |
| 6.076104731708314 | 127.374 | -9.181 | 1.0 |
| 5.9366666186584505 | 112.741 | -11.406 | 1.0 |
| 6.154109852370004 | 128.497 | -11.879 | 1.0 |
| 5.460239895734497 | 90.061 | -9.326 | 1.0 |
| 3.461631553971685 | 30.044 | -18.309 | 1.0 |
| 5.696055968604025 | 141.989 | -6.872 | 0.0 |
| 6.272281433001033 | 95.314 | -15.598 | 1.0 |
| 5.576812820943777 | 85.975 | -3.637 | 1.0 |
| 5.321138423993106 | 61.559 | -25.038 | 1.0 |
| 5.40026758875988 | 152.803 | -4.389 | 0.0 |
| 4.922922798653241 | 96.243 | -10.633 | 1.0 |
| 5.43047442145297 | 147.8 | -8.369 | 0.0 |
| 5.2307437909554215 | 184.033 | -8.551 | 0.0 |
| 5.414322645152568 | 125.988 | -7.567 | 1.0 |
| 6.26401140791294 | 93.605 | -12.174 | 1.0 |
| 5.301412322227913 | 134.362 | -4.411 | 0.0 |
| 5.261701824986476 | 125.159 | -21.073 | 1.0 |
| 5.601228129128594 | 104.393 | -16.924 | 1.0 |
| 5.1183087927453625 | 93.306 | -13.555 | 1.0 |
| 5.415717351071923 | 96.073 | -7.381 | 1.0 |
| 5.739836062373653 | 100.074 | -7.242 | 1.0 |
| 5.619677270652041 | 90.005 | -9.912 | 1.0 |
| 5.207273104763058 | 171.76 | -7.701 | 0.0 |
| 5.574833594834567 | 101.021 | -3.807 | 1.0 |
| 5.013555891662754 | 107.696 | -5.334 | 1.0 |
| 4.077709632470248 | 123.256 | -3.013 | 1.0 |
| 4.852026748288437 | 139.759 | -8.03 | 0.0 |
| 5.303233611985233 | 125.085 | -8.231 | 1.0 |
| 5.789561773504737 | 131.846 | -13.308 | 0.0 |
| 5.439361356314339 | 163.879 | -11.664 | 0.0 |
| 5.353421649883508 | 131.86 | -8.159 | 0.0 |
| 6.152498965263847 | 219.988 | -9.408 | 0.0 |
| 5.541290564401247 | 242.155 | -4.502 | 0.0 |
| 5.502118982831087 | 162.584 | -4.428 | 0.0 |
| 6.022740611873921 | 90.211 | -20.61 | 1.0 |
| 4.6964458745301325 | 153.22 | -5.736 | 0.0 |
| 5.592896350533049 | 105.241 | -5.748 | 1.0 |
| 5.105244864895034 | 197.736 | -14.357 | 0.0 |
| 6.104850529880597 | 126.968 | -13.554 | 1.0 |
| 5.781296264233327 | 65.017 | -8.469 | 1.0 |
| 5.003784346522015 | 115.874 | -10.524 | 1.0 |
| 5.4597954495529875 | 93.984 | -4.948 | 1.0 |
| 5.282218878449877 | 105.617 | -28.85 | 1.0 |
| 5.3889988504485355 | 95.041 | -8.488 | 1.0 |
| 5.161161642533481 | 223.302 | -6.847 | 0.0 |
| 5.1619104721997 | 91.369 | -15.892 | 1.0 |
| 5.663321664900875 | 155.036 | -13.373 | 0.0 |
| 5.867111759061063 | 131.977 | -9.179 | 0.0 |
| 5.839595645377269 | 131.265 | -6.335 | 0.0 |
| 5.609491062398614 | 87.319 | -7.471 | 1.0 |
| 5.274624391653377 | 140.096 | -7.449 | 0.0 |
| 4.771303246638827 | 63.71 | -10.31 | 1.0 |
| 6.026909390629277 | 126.015 | -9.087 | 1.0 |
| 5.460239895734497 | 85.519 | -8.172 | 1.0 |
| 5.032305979406649 | 140.137 | -2.817 | 0.0 |
| 3.8527917624084433 | 111.193 | -20.757 | 1.0 |
| 5.550873690024442 | 100.014 | -8.463 | 1.0 |
| 6.128702349999979 | 126.998 | -9.481 | 1.0 |
| 5.850858502328606 | 96.111 | -14.782 | 1.0 |
| 5.883323391273292 | 97.304 | -6.351 | 1.0 |
| 5.35872276433664 | 86.691 | -4.353 | 1.0 |
| 5.734952664000064 | 114.219 | -3.163 | 1.0 |
| 5.318837824377515 | 116.906 | -17.986 | 1.0 |
| 5.497527627913457 | 89.112 | -2.579 | 1.0 |
| 5.902850659511112 | 130.005 | -5.964 | 1.0 |
| 5.219077332479166 | 98.044 | -8.327 | 1.0 |
| 4.852026748288437 | 56.4 | -26.583 | 1.0 |
| 5.387446848768176 | 153.759 | -7.641 | 0.0 |
| 5.019097058838546 | 93.415 | -7.098 | 1.0 |
| 5.630135755083509 | 174.595 | -7.042 | 0.0 |
| 5.681293243670071 | 160.029 | -7.049 | 0.0 |
| 4.690467127506071 | 109.941 | -12.103 | 1.0 |
| 4.886921078717307 | 156.884 | -8.85 | 0.0 |
| 3.7839566522346817 | 78.948 | -11.663 | 1.0 |
| 5.6159765639264005 | 92.998 | -5.958 | 1.0 |
| 5.723065947607238 | 126.023 | -13.288 | 1.0 |
| 5.11909033676264 | 91.969 | -10.539 | 1.0 |
| 5.639000688376509 | 150.027 | -9.959 | 0.0 |
| 5.283014929190587 | 98.844 | -12.454 | 1.0 |
| 5.334832065977185 | 237.604 | -20.672 | 0.0 |
| 5.6899827955592155 | 122.123 | -7.269 | 1.0 |
| 5.214826784280303 | 123.281 | -5.303 | 1.0 |
| 5.620245418853304 | 104.984 | -8.49 | 1.0 |
| 5.48003820127795 | 154.754 | -7.717 | 0.0 |
| 6.036257303707656 | 132.001 | -9.288 | 0.0 |
| 4.952703169254423 | 126.134 | -9.039 | 1.0 |
| 4.84032586198911 | 85.65 | -12.166 | 1.0 |
| 5.307125163770039 | 107.997 | -7.99 | 1.0 |
| 5.499558971737484 | 131.963 | -23.782 | 0.0 |
| 5.793629272482639 | 102.861 | -15.64 | 1.0 |
| 5.448057577397297 | 106.308 | -4.182 | 1.0 |
| 5.10935559222606 | 108.97 | -7.745 | 1.0 |
| 5.250118909785244 | 131.007 | -12.812 | 0.0 |
| 5.421045753770749 | 187.967 | -5.414 | 0.0 |
| 5.464673526949484 | 88.954 | -19.225 | 1.0 |
| 5.43333140326241 | 91.372 | -21.655 | 1.0 |
| 6.207548860529364 | 86.7 | -13.522 | 1.0 |
| 5.925707203507987 | 126.01 | -7.652 | 1.0 |
| 5.83418384470021 | 125.9 | -10.454 | 1.0 |
| 5.379530281569491 | 166.991 | -10.068 | 0.0 |
| 5.252856399447034 | 132.012 | -7.557 | 0.0 |
| 5.586650576963475 | 92.205 | -12.568 | 1.0 |
| 5.274891818392382 | 156.306 | -11.205 | 0.0 |
| 4.7561445982425585 | 181.832 | -5.36 | 0.0 |
| 6.046013502325825 | 91.214 | -8.872 | 1.0 |
| 5.656954494144676 | 133.205 | -8.568 | 0.0 |
| 5.469307759160471 | 119.99 | -13.474 | 1.0 |
| 5.492807583960489 | 112.015 | -7.135 | 1.0 |
| 5.51881068910637 | 142.751 | -15.649 | 0.0 |
| 4.74006814443605 | 154.067 | -3.63 | 0.0 |
| 5.59425741040241 | 135.616 | -10.088 | 0.0 |
| 5.099525150083379 | 111.869 | -6.919 | 1.0 |
| 5.452993065671398 | 150.525 | -11.524 | 0.0 |
| 5.441174600518283 | 91.374 | -6.715 | 1.0 |
| 5.532751854652075 | 162.048 | -5.726 | 0.0 |
| 5.079569452748531 | 119.939 | -10.924 | 1.0 |
| 5.154849628686833 | 107.51 | -10.176 | 1.0 |
| 5.610543052685526 | 80.025 | -10.408 | 1.0 |
| 5.561470781093142 | 155.746 | -8.475 | 0.0 |
| 5.72885716058872 | 131.971 | -7.201 | 0.0 |
| 5.196630096256507 | 112.52 | -22.935 | 1.0 |
| 6.677072604765031 | 86.596 | -9.254 | 1.0 |
| 5.276628504428926 | 67.183 | -17.95 | 1.0 |
| 5.774423369628363 | 91.166 | -16.059 | 1.0 |
| 5.122366215562913 | 132.742 | -6.337 | 0.0 |
| 4.737096290827358 | 84.435 | -22.572 | 1.0 |
| 5.400975032797074 | 129.749 | -12.142 | 1.0 |
| 5.363628963785261 | 88.222 | -6.461 | 1.0 |
| 5.523722678152987 | 104.592 | -5.825 | 1.0 |
| 5.840811177887705 | 128.373 | -8.574 | 1.0 |
| 5.607958929769235 | 115.338 | -5.719 | 1.0 |
| 5.325088257046393 | 101.662 | -10.225 | 1.0 |
| 6.105142010870422 | 119.992 | -10.292 | 1.0 |
| 5.175588953897928 | 97.619 | -18.724 | 1.0 |
| 5.367170955002224 | 119.032 | -4.0 | 1.0 |
| 5.272884218579013 | 123.695 | -7.73 | 1.0 |
| 5.090079730533548 | 130.771 | -10.753 | 1.0 |
| 4.90527744510154 | 106.427 | -17.549 | 1.0 |
| 5.291204127341661 | 104.378 | -12.13 | 1.0 |
| 4.726046477037809 | 117.436 | -26.354 | 1.0 |
| 4.914522552325214 | 156.8 | -5.616 | 0.0 |
| 3.645320906144505 | 190.071 | -8.348 | 0.0 |
| 6.217912752034596 | 127.986 | -15.299 | 1.0 |
| 5.49699239016298 | 152.552 | -9.21 | 0.0 |
| 4.91854054591316 | 171.014 | -4.493 | 0.0 |
| 5.748533253386812 | 125.986 | -9.382 | 1.0 |
| 5.686799476467082 | 105.387 | -13.275 | 1.0 |
| 5.7666855967574575 | 60.453 | -8.009 | 1.0 |
| 5.378686990009553 | 69.781 | -10.723 | 1.0 |
| 5.305311003955202 | 100.036 | -4.15 | 1.0 |
| 5.504991152805347 | 91.92 | -4.939 | 1.0 |
| 5.271409371809784 | 202.926 | -12.805 | 0.0 |
| 5.584003924284049 | 181.534 | -15.531 | 0.0 |
| 6.215096858959298 | 130.013 | -7.987 | 1.0 |
| 5.487092346805161 | 104.257 | -9.098 | 1.0 |
| 5.754096429647435 | 238.327 | -7.028 | 0.0 |
| 5.236179002727057 | 112.183 | -14.136 | 1.0 |
| 5.452545386241633 | 89.064 | -9.939 | 1.0 |
| 4.571933723436425 | 186.769 | -11.649 | 0.0 |
| 5.74878302284854 | 126.157 | -10.84 | 1.0 |
| 4.462453307085061 | 134.395 | -4.727 | 0.0 |
| 5.229765094411506 | 143.875 | -12.669 | 0.0 |
| 5.516293107703791 | 111.874 | -10.131 | 1.0 |
| 4.986986110278029 | 92.72 | -6.982 | 1.0 |
| 5.134594536181753 | 97.94 | -14.83 | 1.0 |
| 5.94764368547729 | 120.983 | -9.233 | 1.0 |
| 5.589973519371366 | 139.881 | -11.774 | 0.0 |
| 5.832577543586846 | 108.428 | -8.926 | 1.0 |
| 5.57404080623269 | 129.139 | -4.393 | 1.0 |
| 5.737734073968367 | 138.816 | -7.556 | 0.0 |
| 5.228365338789169 | 109.997 | -10.253 | 1.0 |
| 5.478948537110532 | 84.271 | -14.741 | 1.0 |
| 5.097132205437792 | 94.075 | -3.565 | 1.0 |
| 5.7915577015049 | 111.001 | -8.265 | 1.0 |
| 4.758836405626318 | 156.703 | -9.588 | 0.0 |
| 5.268318568522543 | 70.923 | -11.502 | 1.0 |
| 5.101912321360921 | 144.581 | -10.228 | 0.0 |
| 5.607287873701992 | 94.022 | -11.182 | 1.0 |
| 5.464231047351683 | 80.56 | -5.019 | 1.0 |
| 5.82628048105143 | 144.995 | -7.894 | 0.0 |
| 5.11862145971635 | 95.003 | -6.393 | 1.0 |
| 5.226542700885508 | 144.35 | -5.396 | 0.0 |
| 5.764803190566032 | 112.0 | -9.303 | 1.0 |
| 4.7352630988107265 | 114.697 | -4.575 | 1.0 |
| 5.316018719084458 | 141.976 | -21.61 | 0.0 |
| 5.733516795474041 | 183.021 | -11.119 | 0.0 |
| 5.305829678341554 | 86.122 | -4.442 | 1.0 |
| 5.934733177365839 | 124.97 | -18.222 | 1.0 |
| 5.253812766217677 | 96.969 | -3.996 | 1.0 |
| 5.411294114932506 | 89.874 | -6.8 | 1.0 |
| 4.551471791137433 | 80.11 | -12.882 | 1.0 |
| 5.24654895931225 | 126.857 | -11.178 | 1.0 |
| 5.52538935145276 | 88.999 | -7.108 | 1.0 |
| 5.233395411909235 | 174.002 | -4.727 | 0.0 |
| 5.062519840326649 | 172.201 | -4.75 | 0.0 |
| 5.47709335356482 | 130.832 | -19.444 | 1.0 |
| 6.546896021572621 | 175.921 | -14.61 | 0.0 |
| 5.875580463550244 | 125.238 | -8.638 | 1.0 |
| 5.547621681459602 | 182.05 | -8.294 | 0.0 |
| 5.600552539550706 | 165.987 | -5.869 | 0.0 |
| 5.330541303481353 | 100.578 | -13.527 | 1.0 |
| 5.521426437267798 | 98.619 | -8.239 | 1.0 |
| 5.0512127147107515 | 100.524 | -13.862 | 1.0 |
The new clustering model makes much more sense. Songs with high tempo and loudness are put in one cluster and song duration does not affect song categories.
To really understand how the points in 3D behave you need to see them in 3D interactively and understand the limits of its three 2D projections. For this let us spend some time and play in sageMath Worksheet in CoCalc (it is free for light-weight use and perhaps worth the 7 USD a month if you need more serious computing in mathmeatics, statistics, etc. in multiple languages!).
Let us take a look at this sageMath Worksheet published here:
- https://cocalc.com/share/ee9392a2-c83b-4eed-9468-767bb90fd12a/3DEuclideanSpace_1MSongsKMeansClustering.sagews?viewer=share
- and the accompanying datasets (downloaded from the
displays in this notebook and uploaded to CoCalc as CSV files):- https://cocalc.com/projects/ee9392a2-c83b-4eed-9468-767bb90fd12a/files/KMeansClusters10003DFeaturesloudness-tempologDurationOf1MSongsKMeansfor015sds2-2.csv
- https://cocalc.com/projects/ee9392a2-c83b-4eed-9468-767bb90fd12a/files/KMeansClusters10003DFeaturesloudness-tempoDurationOf1MSongsKMeansfor015sds2-2.csv
The point of the above little example is that you need to be able to tell a sensible story with your data science process and not just blindly apply a heuristic, but highly scalable, algorithm which depends on the notion of nearest neighborhoods defined by the metric (Euclidean distances in 3-dimensional real-valued spaces in this example) induced by the features you have engineered or have the power to re/re/...-engineer to increase the meaningfullness of the problem at hand.
Determining Optimal K
There are methods to find the optimal number of clusters. This partly depends on the purpose of the unsupervised learning task.
See here for some metrics in scala for the Irish dataset.
Supervised Clustering with Decision Trees
Visual Introduction to decision trees and application to hand-written digit recognition
SOURCE: This is just a couple of decorations on a notebook published in databricks community edition in 2016.
Decision Trees for handwritten digit recognition
This notebook demonstrates learning a Decision Tree using Spark's distributed implementation. It gives the reader a better understanding of some critical hyperparameters for the tree learning algorithm, using examples to demonstrate how tuning the hyperparameters can improve accuracy.
Background: To learn more about Decision Trees, check out the resources at the end of this notebook. The visual description of ML and Decision Trees provides nice intuition helpful to understand this notebook, and Wikipedia gives lots of details.
Data: We use the classic MNIST handwritten digit recognition dataset. It is from LeCun et al. (1998) and may be found under "mnist" at the LibSVM dataset page.
Goal: Our goal for our data is to learn how to recognize digits (0 - 9) from images of handwriting. However, we will focus on understanding trees, not on this particular learning problem.
Takeaways: Decision Trees take several hyperparameters which can affect the accuracy of the learned model. There is no one "best" setting for these for all datasets. To get the optimal accuracy, we need to tune these hyperparameters based on our data.
Let's Build Intuition for Learning with Decision Trees
- Right-click and open the following link in a new Tab:
- The visual description of ML and Decision Trees which was nominated for a NSF Vizzie award.
Load MNIST training and test datasets
Our datasets are vectors of pixels representing images of handwritten digits. For example:

These datasets are stored in the popular LibSVM dataset format. We will load them using MLlib's LibSVM dataset reader utility.
//-----------------------------------------------------------------------------------------------------------------
// using RDD-based MLlib - ok for Spark 1.x
// MLUtils.loadLibSVMFile returns an RDD.
//import org.apache.spark.mllib.util.MLUtils
//val trainingRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
//val testRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt")
// We convert the RDDs to DataFrames to use with ML Pipelines.
//val training = trainingRDD.toDF()
//val test = testRDD.toDF()
// Note: In Spark 1.6 and later versions, Spark SQL has a LibSVM data source. The above lines can be simplified to:
//// val training = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-train.txt")
//// val test = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-test.txt")
//-----------------------------------------------------------------------------------------------------------------
val training = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
val test = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt")
// Cache data for multiple uses.
training.cache()
test.cache()
println(s"We have ${training.count} training images and ${test.count} test images.")
We have 60000 training images and 10000 test images.
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
test: org.apache.spark.sql.DataFrame = [label: double, features: vector]
Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).
training.printSchema()
root
|-- label: double (nullable = true)
|-- features: vector (nullable = true)
training.show(3) // replace 'true' by 'false' to see the whole row hidden by '...'
+-----+--------------------+
|label| features|
+-----+--------------------+
| 5.0|(780,[152,153,154...|
| 0.0|(780,[127,128,129...|
| 4.0|(780,[160,161,162...|
+-----+--------------------+
only showing top 3 rows
display(training) // this is databricks-specific for interactive visual convenience
The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) or training.show(2,false) above, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):
type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]
Here,
type: 0says we have a sparse vector that only represents non-zero entries (as opposed to a dense vector where every entry is represented).size: 780says the vector has 780 indices in total- these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
indices: [152,153,155,...,682,683]are the indices from the[0,1,...,779]possible indices with non-zero values- a value is an integer encoding the gray-level at the pixel index
values: [3, 18, 18,18,126,...,132,16]are the actual gray level values, for example:- at pixed index
152the gray-level value is3, - at index
153the gray-level value is18, - ..., and finally at
- at index
683the gray-level value is18
- at pixed index
Train a Decision Tree
We begin by training a decision tree using the default settings. Before training, we want to tell the algorithm that the labels are categories 0-9, rather than continuous values. We use the StringIndexer class to do this. We tie this feature preprocessing together with the tree algorithm using a Pipeline. ML Pipelines are tools Spark provides for piecing together Machine Learning algorithms into workflows. To learn more about Pipelines, check out other ML example notebooks in Databricks and the ML Pipelines user guide. Also See mllib-decision-tree.html#basic-algorithm.
// Import the ML algorithms we will use.
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
// StringIndexer: Read input column "label" (digits) and annotate them as categorical values.
val indexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel")
// DecisionTreeClassifier: Learn to predict column "indexedLabel" using the "features" column.
val dtc = new DecisionTreeClassifier().setLabelCol("indexedLabel")
// Chain indexer + dtc together into a single ML Pipeline.
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
indexer: org.apache.spark.ml.feature.StringIndexer = strIdx_cdb9fb742f94
dtc: org.apache.spark.ml.classification.DecisionTreeClassifier = dtc_9345fcaec0f5
pipeline: org.apache.spark.ml.Pipeline = pipeline_a426bd538000
Now, let's fit a model to our data.
val model = pipeline.fit(training)
model: org.apache.spark.ml.PipelineModel = pipeline_a426bd538000
We can inspect the learned tree by displaying it using Databricks ML visualization. (Visualization is available for several but not all models.)
// The tree is the last stage of the Pipeline. Display it!
val tree = model.stages.last.asInstanceOf[DecisionTreeClassificationModel]
display(tree)
| treeNode |
|---|
| {"index":31,"featureType":"continuous","prediction":null,"threshold":133.5,"categories":null,"feature":350,"overflow":false} |
| {"index":15,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":568,"overflow":false} |
| {"index":7,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":430,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":2.5,"categories":null,"feature":405,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":12.5,"categories":null,"feature":485,"overflow":false} |
| {"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":2,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":14.5,"categories":null,"feature":516,"overflow":false} |
| {"index":4,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":34.5,"categories":null,"feature":211,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":98,"overflow":false} |
| {"index":8,"featureType":null,"prediction":8.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":10,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":13,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":156,"overflow":false} |
| {"index":12,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":14,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":23,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":435,"overflow":false} |
| {"index":19,"featureType":"continuous","prediction":null,"threshold":10.5,"categories":null,"feature":489,"overflow":false} |
| {"index":17,"featureType":"continuous","prediction":null,"threshold":2.5,"categories":null,"feature":380,"overflow":false} |
| {"index":16,"featureType":null,"prediction":5.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":18,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":21,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":320,"overflow":false} |
| {"index":20,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":22,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":27,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":346,"overflow":false} |
| {"index":25,"featureType":"continuous","prediction":null,"threshold":97.5,"categories":null,"feature":348,"overflow":false} |
| {"index":24,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":26,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":29,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":655,"overflow":false} |
| {"index":28,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":30,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":47,"featureType":"continuous","prediction":null,"threshold":36.5,"categories":null,"feature":489,"overflow":false} |
| {"index":39,"featureType":"continuous","prediction":null,"threshold":26.5,"categories":null,"feature":290,"overflow":false} |
| {"index":35,"featureType":"continuous","prediction":null,"threshold":100.5,"categories":null,"feature":486,"overflow":false} |
| {"index":33,"featureType":"continuous","prediction":null,"threshold":129.5,"categories":null,"feature":490,"overflow":false} |
| {"index":32,"featureType":null,"prediction":2.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":34,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":37,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":656,"overflow":false} |
| {"index":36,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":38,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":43,"featureType":"continuous","prediction":null,"threshold":5.5,"categories":null,"feature":297,"overflow":false} |
| {"index":41,"featureType":"continuous","prediction":null,"threshold":169.5,"categories":null,"feature":486,"overflow":false} |
| {"index":40,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":42,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":45,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":598,"overflow":false} |
| {"index":44,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":46,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":55,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":521,"overflow":false} |
| {"index":51,"featureType":"continuous","prediction":null,"threshold":7.5,"categories":null,"feature":347,"overflow":false} |
| {"index":49,"featureType":"continuous","prediction":null,"threshold":4.5,"categories":null,"feature":206,"overflow":false} |
| {"index":48,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":50,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":53,"featureType":"continuous","prediction":null,"threshold":16.5,"categories":null,"feature":514,"overflow":false} |
| {"index":52,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":54,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":59,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":658,"overflow":false} |
| {"index":57,"featureType":"continuous","prediction":null,"threshold":8.5,"categories":null,"feature":555,"overflow":false} |
| {"index":56,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":58,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":61,"featureType":"continuous","prediction":null,"threshold":12.5,"categories":null,"feature":543,"overflow":false} |
| {"index":60,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":62,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
Above, we can see how the tree makes predictions. When classifying a new example, the tree starts at the "root" node (at the top). Each tree node tests a pixel value and goes either left or right. At the bottom "leaf" nodes, the tree predicts a digit as the image's label.
Hyperparameter Tuning
Run the next cell and come back into hyper-parameter tuning for a couple minutes.
Exploring "maxDepth": training trees of different sizes
In this section, we test tuning a single hyperparameter maxDepth, which determines how deep (and large) the tree can be. We will train trees at varying depths and see how it affects the accuracy on our held-out test set.
Note: The next cell can take about 1 minute to run since it is training several trees which get deeper and deeper.
val variedMaxDepthModels = (0 until 8).map { maxDepth =>
// For this setting of maxDepth, learn a decision tree.
dtc.setMaxDepth(maxDepth)
// Create a Pipeline with our feature processing stage (indexer) plus the tree algorithm
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
// Run the ML Pipeline to learn a tree.
pipeline.fit(training)
}
variedMaxDepthModels: scala.collection.immutable.IndexedSeq[org.apache.spark.ml.PipelineModel] = Vector(pipeline_e96074b3bded, pipeline_12646faf8260, pipeline_d5f639d6a180, pipeline_4034c9138161, pipeline_83215a1ed684, pipeline_8737125f5dcc, pipeline_02a6caa4d542, pipeline_88827be266ae)
We will use the default metric to evaluate the performance of our classifier:
// Define an evaluation metric. In this case, we will use "accuracy".
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setMetricName("f1") // default MetricName
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_0ee0afcb7621, metricName=f1, metricLabel=0.0, beta=1.0, eps=1.0E-15
// For each maxDepth setting, make predictions on the test data, and compute the classifier's f1 performance metric.
val f1MetricPerformanceMeasures = (0 until 8).map { maxDepth =>
val model = variedMaxDepthModels(maxDepth)
// Calling transform() on the test set runs the fitted pipeline.
// The learned model makes predictions on each test example.
val predictions = model.transform(test)
// Calling evaluate() on the predictions DataFrame computes our performance metric.
(maxDepth, evaluator.evaluate(predictions))
}.toDF("maxDepth", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxDepth: int, f1: double]
We can display our accuracy results and see immediately that deeper, larger trees are more powerful classifiers, achieving higher accuracies.
Note: When you run f1MetricPerformanceMeasures.show(), you will get a table with f1 score getting better (i.e., approaching 1) with depth.
f1MetricPerformanceMeasures.show()
+--------+-------------------+
|maxDepth| f1|
+--------+-------------------+
| 0| 0.023138302649304|
| 1|0.07692344325297736|
| 2|0.21454218035530098|
| 3|0.43262516590643385|
| 4| 0.5918539983626627|
| 5| 0.6806954435278861|
| 6| 0.7478698562916142|
| 7| 0.7876393954574569|
+--------+-------------------+
Even though deeper trees are more powerful, they are not always better (recall from the SF/NYC city classification from house features at The visual description of ML and Decision Trees). If we kept increasing the depth on a rich enough dataset, training would take longer and longer. We also might risk overfitting (fitting the training data so well that our predictions get worse on test data); it is important to tune parameters based on held-out data to prevent overfitting. This will ensure that the fitted model generalizes well to yet unseen data, i.e. minimizes generalization error in a mathematical statistical sense.
Exploring "maxBins": discretization for efficient distributed computing
This section explores a more expert-level setting maxBins. For efficient distributed training of Decision Trees, Spark and most other libraries discretize (or "bin") continuous features (such as pixel values) into a finite number of values. This is an important step for the distributed implementation, but it introduces a tradeoff: Larger maxBins mean your data will be more accurately represented, but it will also mean more communication (and slower training).
The default value of maxBins generally works, but it is interesting to explore on our handwritten digit dataset. Remember our digit image from above:

It is grayscale. But if we set maxBins = 2, then we are effectively making it a black-and-white image, not grayscale. Will that affect the accuracy of our model? Let's see!
Note: The next cell can take about 35 seconds to run since it trains several trees. Read the details on maxBins at mllib-decision-tree.html#split-candidates.
dtc.setMaxDepth(6) // Set maxDepth to a reasonable value.
// now try the maxBins "hyper-parameter" which actually acts as a "coarsener"
// mathematical researchers should note that it is a sub-algebra of the finite
// algebra of observable pixel images at the finest resolution available to us
// giving a compression of the image to fewer coarsely represented pixels
val f1MetricPerformanceMeasures = Seq(2, 4, 8, 16, 32).map { case maxBins =>
// For this value of maxBins, learn a tree.
dtc.setMaxBins(maxBins)
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
val model = pipeline.fit(training)
// Make predictions on test data, and compute accuracy.
val predictions = model.transform(test)
(maxBins, evaluator.evaluate(predictions))
}.toDF("maxBins", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxBins: int, f1: double]
f1MetricPerformanceMeasures.show()
+-------+------------------+
|maxBins| f1|
+-------+------------------+
| 2|0.7429289482693366|
| 4|0.7390759023032782|
| 8|0.7443669963407805|
| 16|0.7426765688669141|
| 32|0.7478698562916142|
+-------+------------------+
We can see that extreme discretization (black and white) hurts performance as measured by F1-error, but only a bit. Using more bins increases the accuracy (but also makes learning more costly).
What's next?
- Explore: Try out tuning other parameters of trees---or even ensembles like Random Forests or Gradient-Boosted Trees.
- Automated tuning: This type of tuning does not have to be done by hand. (We did it by hand here to show the effects of tuning in detail.) MLlib provides automated tuning functionality via
CrossValidator. Check out the other Databricks ML Pipeline guides or the Spark ML user guide for details.
Resources
If you are interested in learning more on these topics, these resources can get you started:
- Excellent visual description of Machine Learning and Decision Trees
- This gives an intuitive visual explanation of ML, decision trees, overfitting, and more.
- Blog post on MLlib Random Forests and Gradient-Boosted Trees
- Random Forests and Gradient-Boosted Trees combine many trees into more powerful ensemble models. This is the original post describing MLlib's forest and GBT implementations.
- Wikipedia
Linear Algebra Review
This is a breeze'y scalarific break-down of:
- Home Work: read this! https://github.com/scalanlp/breeze/wiki/Quickstart
Using the above resources we'll provide a review of basic linear algebra concepts that will recur throughout the course. These concepts include:
- Matrices
- Vectors
- Arithmetic operations with vectors and matrices
We will see the accompanying Scala computations in the local or non-distributed setting.
Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications, eigen systems with real and comples Eigen values NOW from the following interactive visual-cognitive aid at:
- http://setosa.io/ev/eigenvectors-and-eigenvalues/ just focus on geometric interpretation of vectors and matrices in Cartesian coordinates.
Breeze is a linear algebra package in Scala. First, let us import it as follows:
import breeze.linalg._
import breeze.linalg._
1. Matrix: creation and element-access
A matrix is a two-dimensional array.
Let us denote matrices via bold uppercase letters as follows:
For instance, the matrix below is denoted with \(\mathbf{A}\), a capital bold A.
\[ \mathbf{A} = \begin{pmatrix} a_{11} & a_{12} & a_{13} \ a_{21} & a_{22} & a_{23} \ a_{31} & a_{32} & a_{33} \end{pmatrix} \]
We usually put commas between the row and column indexing sub-scripts, to make the possibly multi-digit indices distinguishable as follows:
\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \ a_{3,1} & a_{3,2} & a_{3,3} \end{pmatrix} \]
- \(\mathbf{A}_{i,j}\) denotes the entry in \(i\)-th row and \(j\)-th column of the matrix \(\mathbf{A}\).
- So for instance,
- the first entry, the top left entry, is denoted by \(\mathbf{A}_{1,1}\).
- And the entry in the third row and second column is denoted by \(\mathbf{A}_{3,2}\).
- We say that a matrix with n rows and m columns is an \(n\) by \(m\) matrix and written as \(n \times m\)
- The matrix \(\mathbf{A}\) shown above is a generic \(3 \times 3\) (pronounced 3-by-3) matrix.
- And the matrix in Ameet's example in the video above, having 4 rows and 3 columns, is a 4 by 3 matrix.
- If a matrix \(\mathbf{A}\) is \(n \times m\), we write:
- \(\mathbf{A} \in \mathbb{R}^{n \times m}\) and say that \(\mathbf{A}\) is an \(\mathbb{R}\) to the power of the n times m,
- where, \(\mathbb{R}\) here denotes the set of all real numbers in the line given by the open interval: \((-\infty,+\infty)\).
- \(\mathbf{A} \in \mathbb{R}^{n \times m}\) and say that \(\mathbf{A}\) is an \(\mathbb{R}\) to the power of the n times m,
Let us created a matrix A as a val (that is immutable) in scala. The matrix we want to create is mathematically notated as follows:
\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \end{pmatrix}
\begin{pmatrix} 1 & 2 & 3 \ 4 & 5 & 6 \end{pmatrix} \]
val A = DenseMatrix((1, 2, 3), (4, 5, 6)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
A.size
res0: Int = 6
A.rows // number of rows
res1: Int = 2
A.size / A.rows // num of columns
res2: Int = 3
A.cols // also say
res3: Int = 3
Now, let's access the element \(a_{1,1}\), i.e., the element from the first row and first column of \(\mathbf{A}\), which in our val A matrix is the integer of type Int equalling 1.
A(0, 0) // Remember elements are indexed by zero in scala
res4: Int = 1
Gotcha: indices in breeze matrices start at 0 as in numpy of python and not at 1 as in MATLAB!
Of course if you assign the same dense matrix to a mutable var B then its entries can be modified as follows:
var B = DenseMatrix((1, 2, 3), (4, 5, 6))
B: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
B(0,0)=999; B(1,1)=969; B(0,2)=666
B
res5: breeze.linalg.DenseMatrix[Int] =
999 2 666
4 969 6
-
A vector is a matrix with many rows and one column.
-
We'll denote a vector by bold lowercase letters: \[\mathbf{a} = \begin{pmatrix} 3.3 \ 1.0 \ 6.3 \ 3.6 \end{pmatrix}\]
So, the vector above is denoted by \(\mathbf{a}\), the lowercase, bold a.
-
\(a_i\) denotes the i-th entry of a vector. So for instance:
- \(a_2\) denotes the second entry of the vector and it is 1.0 for our vector.
-
If a vector is m-dimensional, then we say that \(\mathbf{a}\) is in \(\mathbb{R}^m\) and write \(\mathbf{a} \in \ \mathbb{R}^m\).
- So our \(\mathbf{a} \in \ \mathbb{R}^4\).
val a = DenseVector(3.3, 1.0, 6.3, 3.6) // these are row vectors
a: breeze.linalg.DenseVector[Double] = DenseVector(3.3, 1.0, 6.3, 3.6)
a.size // a is a column vector of size 4
res6: Int = 4
a(1) // the second element of a is indexed by 1 as the first element is indexed by 0
res7: Double = 1.0
val a = DenseVector[Double](5, 4, -1) // this makes a vector of Doubles from input Int
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val a = DenseVector(5.0, 4.0, -1.0) // this makes a vector of Doubles from type inference . NOTE "5.0" is needed not just "5."
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val x = DenseVector.zeros[Double](5) // this will output x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
val A = DenseMatrix((1, 4), (6, 1), (3, 5)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
A.t // transpose of A
res8: breeze.linalg.DenseMatrix[Int] =
1 6 3
4 1 5
val a = DenseVector(3.0, 4.0, 1.0)
a: breeze.linalg.DenseVector[Double] = DenseVector(3.0, 4.0, 1.0)
a.t
res9: breeze.linalg.Transpose[breeze.linalg.DenseVector[Double]] = Transpose(DenseVector(3.0, 4.0, 1.0))
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
val B = -A
B: breeze.linalg.DenseMatrix[Int] =
-1 -4
-6 -1
-3 -5
A + B // should be A-A=0
res10: breeze.linalg.DenseMatrix[Int] =
0 0
0 0
0 0
A - B // should be A+A=2A
res11: breeze.linalg.DenseMatrix[Int] =
2 8
12 2
6 10
B - A // should be -A-A=-2A
res12: breeze.linalg.DenseMatrix[Int] =
-2 -8
-12 -2
-6 -10
Operators
All Tensors support a set of operators, similar to those used in Matlab or Numpy.
For HOMEWORK see: Workspace -> scalable-data-science -> xtraResources -> LinearAlgebra -> LAlgCheatSheet for a list of most of the operators and various operations.
Some of the basic ones are reproduced here, to give you an idea.
| Operation | Breeze | Matlab | Numpy |
|---|---|---|---|
| Elementwise addition | a + b | a + b | a + b |
| Elementwise multiplication | a :* b | a .* b | a * b |
| Elementwise comparison | a :< b | a < b (gives matrix of 1/0 instead of true/false) | a < b |
| Inplace addition | a :+= 1.0 | a += 1 | a += 1 |
| Inplace elementwise multiplication | a :*= 2.0 | a *= 2 | a *= 2 |
| Vector dot product | a dot b,a.t * b† | dot(a,b) | dot(a,b) |
| Elementwise sum | sum(a) | sum(sum(a)) | a.sum() |
| Elementwise max | a.max | max(a) | a.max() |
| Elementwise argmax | argmax(a) | argmax(a) | a.argmax() |
| Ceiling | ceil(a) | ceil(a) | ceil(a) |
| Floor | floor(a) | floor(a) | floor(a) |
Pop Quiz:
- what is a natural geometric interpretation of scalar multiplication of a vector or a matrix and what about vector matrix multiplication?
Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications from the first interactive visual-cognitive aid at:
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
5 * A
res13: breeze.linalg.DenseMatrix[Int] =
5 20
30 5
15 25
A * 5
res14: breeze.linalg.DenseMatrix[Int] =
5 20
30 5
15 25
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
val B = DenseMatrix((3, 1), (2, 2), (1, 3))
B: breeze.linalg.DenseMatrix[Int] =
3 1
2 2
1 3
A *:* B // element-wise multiplication
res15: breeze.linalg.DenseMatrix[Int] =
3 4
12 2
3 15
val A = DenseMatrix((1, 4), (3, 1))
A: breeze.linalg.DenseMatrix[Int] =
1 4
3 1
val a = DenseVector(1, -1) // is a column vector
a: breeze.linalg.DenseVector[Int] = DenseVector(1, -1)
a.size // a is a column vector of size 2
res16: Int = 2
A * a
res17: breeze.linalg.DenseVector[Int] = DenseVector(-3, 2)
val A = DenseMatrix((1,2,3),(1,1,1))
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
1 1 1
val B = DenseMatrix((4, 1), (9, 2), (8, 9))
B: breeze.linalg.DenseMatrix[Int] =
4 1
9 2
8 9
A*B // 4+18+14
res18: breeze.linalg.DenseMatrix[Int] =
46 32
21 12
1*4 + 2*9 + 3*8 // checking first entry of A*B
res19: Int = 46
val A = DenseMatrix((1,2,3),(4,5,6))
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
DenseMatrix.eye[Int](3)
res20: breeze.linalg.DenseMatrix[Int] =
1 0 0
0 1 0
0 0 1
A * DenseMatrix.eye[Int](3)
res21: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
DenseMatrix.eye[Int](2) * A
res22: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
val D = DenseMatrix((2.0, 3.0), (4.0, 5.0))
val Dinv = inv(D)
D: breeze.linalg.DenseMatrix[Double] =
2.0 3.0
4.0 5.0
Dinv: breeze.linalg.DenseMatrix[Double] =
-2.5 1.5
2.0 -1.0
D * Dinv
res23: breeze.linalg.DenseMatrix[Double] =
1.0 0.0
0.0 1.0
Dinv * D
res24: breeze.linalg.DenseMatrix[Double] =
1.0 0.0
0.0 1.0
val b = DenseVector(4, 3)
norm(b)
b: breeze.linalg.DenseVector[Int] = DenseVector(4, 3)
res25: Double = 5.0
Math.sqrt(4*4 + 3*3) // check
res26: Double = 5.0
HOMEWORK: read this! https://github.com/scalanlp/breeze/wiki/Quickstart
It is here in markdown'd via wget and pandoc for your convenience.
Scala / nlp / breeze / Quickstart
David Hall edited this page on 24 Dec 2015
Breeze is modeled on Scala, and so if you're familiar with it, you'll be familiar with Breeze. First, import the linear algebra package:
scala> import breeze.linalg._
Let's create a vector:
scala> val x = DenseVector.zeros[Double](5)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
Here we make a column vector of zeros of type Double. And there are other ways we could create the vector - such as with a literal DenseVector(1,2,3) or with a call to fill or tabulate. The vector is "dense" because it is backed by an Array[Double], but could as well have created a SparseVector.zeros[Double](5), which would not allocate memory for zeros.
Unlike Scalala, all Vectors are column vectors. Row vectors are represented as Transpose[Vector[T]].
The vector object supports accessing and updating data elements by their index in 0 to x.length-1. Like Numpy, negative indices are supported, with the semantics that for an index i < 0 we operate on the i-th element from the end (x(i) == x(x.length + i)).
scala> x(0)
Double = 0.0
scala> x(1) = 2
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.0, 0.0)
Breeze also supports slicing. Note that slices using a Range are much, much faster than those with an arbitrary sequence.
scala> x(3 to 4) := .5
breeze.linalg.DenseVector[Double] = DenseVector(0.5, 0.5)
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.5, 0.5)
The slice operator constructs a read-through and write-through view of the given elements in the underlying vector. You set its values using the vectorized-set operator :=. You could as well have set it to a compatibly sized Vector.
scala> x(0 to 1) := DenseVector(.1,.2)
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.1, 0.2, 0.0, 0.5, 0.5)
Similarly, a DenseMatrix can be created with a constructor method call, and its elements can be accessed and updated.
scala> val m = DenseMatrix.zeros[Int](5,5)
m: breeze.linalg.DenseMatrix[Int] =
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
The columns of m can be accessed as DenseVectors, and the rows as DenseMatrices.
scala> (m.rows, m.cols)
(Int, Int) = (5,5)
scala> m(::,1)
breeze.linalg.DenseVector[Int] = DenseVector(0, 0, 0, 0, 0)
scala> m(4,::) := DenseVector(1,2,3,4,5).t // transpose to match row shape
breeze.linalg.DenseMatrix[Int] = 1 2 3 4 5
scala> m
breeze.linalg.DenseMatrix[Int] =
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
1 2 3 4 5
Assignments with incompatible cardinality or a larger numeric type won't compile.
scala> m := x
<console>:13: error: could not find implicit value for parameter op: breeze.linalg.operators.BinaryUpdateOp[breeze.linalg.DenseMatrix[Int],breeze.linalg.DenseVector[Double],breeze.linalg.operators.OpSet]
m := x
^
Assignments with incompatible size will throw an exception:
scala> m := DenseMatrix.zeros[Int](3,3)
java.lang.IllegalArgumentException: requirement failed: Matrices must have same number of row
Sub-matrices can be sliced and updated, and literal matrices can be specified using a simple tuple-based syntax. Unlike Scalala, only range slices are supported, and only the columns (or rows for a transposed matrix) can have a Range step size different from 1.
scala> m(0 to 1, 0 to 1) := DenseMatrix((3,1),(-1,-2))
breeze.linalg.DenseMatrix[Int] =
3 1
-1 -2
scala> m
breeze.linalg.DenseMatrix[Int] =
3 1 0 0 0
-1 -2 0 0 0
0 0 0 0 0
0 0 0 0 0
1 2 3 4 5
Broadcasting
Sometimes we want to apply an operation to every row or column of a matrix, as a unit. For instance, you might want to compute the mean of each row, or add a vector to every column. Adapting a matrix so that operations can be applied column-wise or row-wise is called broadcasting. Languages like R and numpy automatically and implicitly do broadcasting, meaning they won't stop you if you accidentally add a matrix and a vector. In Breeze, you have to signal your intent using the broadcasting operator *. The * is meant to evoke "foreach" visually. Here are some examples:
scala> import breeze.stats.mean
scala> val dm = DenseMatrix((1.0,2.0,3.0),
(4.0,5.0,6.0))
scala> val res = dm(::, *) + DenseVector(3.0, 4.0)
breeze.linalg.DenseMatrix[Double] =
4.0 5.0 6.0
8.0 9.0 10.0
scala> res(::, *) := DenseVector(3.0, 4.0)
scala> res
breeze.linalg.DenseMatrix[Double] =
3.0 3.0 3.0
4.0 4.0 4.0
scala> mean(dm(*, ::))
breeze.linalg.DenseVector[Double] = DenseVector(2.0, 5.0)
breeze.stats.distributions
Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.
scala> import breeze.stats.distributions._
scala> val poi = new Poisson(3.0);
poi: breeze.stats.distributions.Poisson = <function1>
scala> val s = poi.sample(5);
s: IndexedSeq[Int] = Vector(5, 4, 5, 7, 4)
scala> s map { poi.probabilityOf(_) }
IndexedSeq[Double] = Vector(0.10081881344492458, 0.16803135574154085, 0.10081881344492458, 0.02160403145248382, 0.16803135574154085)
scala> val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = breeze.stats.distributions.Rand$$anon$11@1b52e04
scala> breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.9960000000000067,2.9669509509509533,1000)
scala> (poi.mean,poi.variance)
(Double, Double) = (3.0,3.0)
NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.
scala> val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)
scala> expo.rate
Double = 0.5
A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.
scala> expo.probability(0, log(2) * expo.rate)
Double = 0.5
scala> expo.probability(0.0, 1.5)
Double = 0.950212931632136
This means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well
scala> 1 - exp(-3.0)
Double = 0.950212931632136
scala> val samples = expo.sample(2).sorted;
samples: IndexedSeq[Double] = Vector(1.1891135726280517, 2.325607782657507)
scala> expo.probability(samples(0), samples(1));
Double = 0.08316481553047272
scala> breeze.stats.meanAndVariance(expo.samples.take(10000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.029351863973081,4.163267835527843,10000)
scala> (1 / expo.rate, 1 / (expo.rate * expo.rate))
(Double, Double) = (2.0,4.0)
breeze.optimize
TODO: document breeze.optimize.minimize, recommend that instead.
Breeze's optimization package includes several convex optimization routines and a simple linear program solver. Convex optimization routines typically take a DiffFunction[T], which is a Function1 extended to have a gradientAt method, which returns the gradient at a particular point. Most routines will require a breeze.linalg-enabled type: something like a Vector or a Counter.
Here's a simple DiffFunction: a parabola along each vector's coordinate.
scala> import breeze.optimize._
scala> val f = new DiffFunction[DenseVector[Double]] {
| def calculate(x: DenseVector[Double]) = {
| (norm((x - 3d) :^ 2d,1d),(x * 2d) - 6d);
| }
| }
f: java.lang.Object with breeze.optimize.DiffFunction[breeze.linalg.DenseVector[Double]] = $anon$1@617746b2
Note that this function takes its minimum when all values are 3. (It's just a parabola along each coordinate.)
scala> f.valueAt(DenseVector(3,3,3))
Double = 0.0
scala> f.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(0.0, -6.0, -4.0)
scala> f.calculate(DenseVector(0,0))
(Double, breeze.linalg.DenseVector[Double]) = (18.0,DenseVector(-6.0, -6.0))
You can also use approximate derivatives, if your function is easy enough to compute:
scala> def g(x: DenseVector[Double]) = (x - 3.0):^ 2.0 sum
scala> g(DenseVector(0.,0.,0.))
Double = 27.0
scala> val diffg = new ApproximateGradientFunction(g)
scala> diffg.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(1.000000082740371E-5, -5.999990000127297, -3.999990000025377)
Ok, now let's optimize f. The easiest routine to use is just LBFGS, which is a quasi-Newton method that works well for most problems.
scala> val lbfgs = new LBFGS[DenseVector[Double]](maxIter=100, m=3) // m is the memory. anywhere between 3 and 7 is fine. The larger m, the more memory is needed.
scala> val optimum = lbfgs.minimize(f,DenseVector(0,0,0))
optimum: breeze.linalg.DenseVector[Double] = DenseVector(2.9999999999999973, 2.9999999999999973, 2.9999999999999973)
scala> f(optimum)
Double = 2.129924444096732E-29
That's pretty close to 0! You can also use a configurable optimizer, using FirstOrderMinimizer.OptParams. It takes several parameters:
case class OptParams(batchSize:Int = 512,
regularization: Double = 1.0,
alpha: Double = 0.5,
maxIterations:Int = -1,
useL1: Boolean = false,
tolerance:Double = 1E-4,
useStochastic: Boolean= false) {
// ...
}
batchSize applies to BatchDiffFunctions, which support using small minibatches of a dataset. regularization integrates L2 or L1 (depending on useL1) regularization with constant lambda. alpha controls the initial stepsize for algorithms that need it. maxIterations is the maximum number of gradient steps to be taken (or -1 for until convergence). tolerance controls the sensitivity of the convergence check. Finally, useStochastic determines whether or not batch functions should be optimized using a stochastic gradient algorithm (using small batches), or using LBFGS (using the entire dataset).
OptParams can be controlled using breeze.config.Configuration, which we described earlier.
breeze.optimize.linear
We provide a DSL for solving linear programs, using Apache's Simplex Solver as the backend. This package isn't industrial strength yet by any means, but it's good for simple problems. The DSL is pretty simple:
import breeze.optimize.linear._
val lp = new LinearProgram()
import lp._
val x0 = Real()
val x1 = Real()
val x2 = Real()
val lpp = ( (x0 + x1 * 2 + x2 * 3 )
subjectTo ( x0 * -1 + x1 + x2 <= 20)
subjectTo ( x0 - x1 * 3 + x2 <= 30)
subjectTo ( x0 <= 40 )
)
val result = maximize( lpp)
assert( norm(result.result - DenseVector(40.0,17.5,42.5), 2) < 1E-4)
We also have specialized routines for bipartite matching (KuhnMunkres and CompetitiveLinking) and flow problems.
Where to go next?
After reading this quickstart, you can go to other wiki pages, especially Linear Algebra Cheat-Sheet and Data Structures.
Review the following for example:
if you have not experienced or forgotten big-O and big-Omega and big-Theta notation to study the computational efficiency of algorithms.
Let us visit an interactive visual cognitive tool for the basics ideas in linear regression:
Linear Regression by Hastie and Tibshirani
Chapter 3 of https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
The following video playlist is a very concise and thorough treatment of linear regression for those who have taken the 200-level linear algebra. Others can fully understand it with some effort and revisiting.
Ridge regression has a Bayesian interpretation where the weights have a zero-mean Gaussian prior. See 7.5 in Murphy's Machine Learning: A Probabilistic Perspective for details.
Please take notes in mark-down if you want.
For latex math within markdown you can do the following for in-line maths: \(\mathbf{A}_{i,j} \in \mathbb{R}^1\). And to write maths in display mode do the following:
\[\mathbf{A} \in \mathbb{R}^{m \times d} \]
You will need to write such notes for your final project presentation!
Gradient Descent
Refresher: what-is-gradient-descent
Getting our hands dirty with Darren Wilkinson's blog on regression
Let's follow the exposition in:
- https://darrenjw.wordpress.com/2017/02/08/a-quick-introduction-to-apache-spark-for-statisticians/
- https://darrenjw.wordpress.com/2017/06/21/scala-glm-regression-modelling-in-scala/
You need to scroll down the fitst link embedded in iframe below to get to the section on Analysis of quantitative data with Descriptive statistics and Linear regression sub-sections (you can skip the earlier part that shows you how to run everything locally in spark-shell - try this on your own later).
Let's do this live now...
import breeze.stats.distributions._
def x = Gaussian(1.0,2.0).sample(10000)
val xRdd = sc.parallelize(x)
import breeze.stats.distributions._
x: IndexedSeq[Double]
xRdd: org.apache.spark.rdd.RDD[Double] = ParallelCollectionRDD[0] at parallelize at command-2971213210277124:3
println(xRdd.mean)
println(xRdd.sampleVariance)
0.9961211853107911
4.025384520599017
val xStats = xRdd.stats
xStats.mean
xStats.sampleVariance
xStats.sum
xStats: org.apache.spark.util.StatCounter = (count: 10000, mean: 0.996121, stdev: 2.006236, max: 8.326542, min: -6.840659)
res1: Double = 9961.21185310791
val x2 = Gaussian(0.0,1.0).sample(10000) // 10,000 Gaussian samples with mean 0.0 and std dev 1.0
val xx = x zip x2 // creating tuples { (x,x2)_1, ... , (x,x2)_10000}
val lp = xx map {p => 2.0*p._1 + 1.0*p._2 + 1.5} // { lp_i := (2.0*x + 1.0 * x2 + 1.5)_i, i=1,...,10000 }
val eps = Gaussian(0.0,1.0).sample(10000) // standrad normal errors
val y = (lp zip eps) map (p => p._1 + p._2)
val yx = (y zip xx) map (p => (p._1, p._2._1, p._2._2))
x2: IndexedSeq[Double] = Vector(0.1510498720110515, -0.21709023201809416, -0.9420189342233366, -0.6050521762158899, 0.48771393128636853, 0.027986752952643124, 0.08206698908429294, -0.7810931162704136, -1.2057806605697985, -0.9176042669882863, 1.1959113751272468, 0.5771223111098949, -0.5030579509564024, 0.05792512611966754, 2.0492117764647917, -1.6192400586380957, -0.28568171141353893, 1.662660432138664, -0.3053683539449641, 0.3879563181609168, -0.7733054017369991, -0.39556455561853454, 1.7717311537878704, 0.33232937623812936, -0.1392031424321576, -0.5587152832826312, -1.7575839385785728, -0.6579581256900572, 2.037190332203657, 0.03462069676057432, -0.3795037160416964, 0.4622268686186779, -1.0897313199516543, -1.6880936202156007, -0.19374927120525648, -0.5509651353004006, -1.7588836379571422, -0.28930357427256465, 0.4020360571731675, -1.6301541413671794, 0.5958281089725639, 0.366330814768569, 0.4073858014158006, 0.16855406345881346, -0.947375008877488, -1.301502215739479, 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val rddLR = sc.parallelize(yx)
rddLR.take(5)
rddLR: org.apache.spark.rdd.RDD[(Double, Double, Double)] = ParallelCollectionRDD[4] at parallelize at command-2971213210277128:1
res2: Array[(Double, Double, Double)] = Array((5.677461671611596,1.6983876233282715,0.1510498720110515), (6.5963106438672625,1.9306937118935266,-0.21709023201809416), (-6.457080480280965,-3.613579039093624,-0.9420189342233366), (5.725199404014689,1.4774128627063305,-0.6050521762158899), (5.2485124653727215,0.49536920319327216,0.48771393128636853))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show
dfLR.show(5)
+-------------------+--------------------+--------------------+
| y| x1| x2|
+-------------------+--------------------+--------------------+
| 5.677461671611596| 1.6983876233282715| 0.1510498720110515|
| 6.5963106438672625| 1.9306937118935266|-0.21709023201809416|
| -6.457080480280965| -3.613579039093624| -0.9420189342233366|
| 5.725199404014689| 1.4774128627063305| -0.6050521762158899|
| 5.2485124653727215| 0.49536920319327216| 0.48771393128636853|
| 6.147100844408568| 2.619266385545484|0.027986752952643124|
|-0.7268042607303868| -1.6216138344745752| 0.08206698908429294|
| 5.5363351738375695| 1.2511380715559544| -0.7810931162704136|
|-0.1828379004896784| -0.6891022233658615| -1.2057806605697985|
| 6.488199218865876| 2.7890847095863527| -0.9176042669882863|
| 1.3969322930903634| -0.8384317817611013| 1.1959113751272468|
| 1.468434023004454|-0.39702300426389736| 0.5771223111098949|
|-0.6234341966751746|-0.20341459413042506| -0.5030579509564024|
| 6.452061108539848| 1.940354690386933| 0.05792512611966754|
| 9.199371261060582| 3.1686700336304794| 2.0492117764647917|
|0.07256979343692907|-0.01571825151138...| -1.6192400586380957|
| 9.589528428542355| 3.5222633033548227|-0.28568171141353893|
| 1.1696257215909478| -1.7700416261712641| 1.662660432138664|
|-0.8138719762638745| -1.276616299487233| -0.3053683539449641|
| 9.309926019422797| 2.847017612404211| 0.3879563181609168|
+-------------------+--------------------+--------------------+
only showing top 20 rows
+------------------+-------------------+--------------------+
| y| x1| x2|
+------------------+-------------------+--------------------+
| 5.677461671611596| 1.6983876233282715| 0.1510498720110515|
|6.5963106438672625| 1.9306937118935266|-0.21709023201809416|
|-6.457080480280965| -3.613579039093624| -0.9420189342233366|
| 5.725199404014689| 1.4774128627063305| -0.6050521762158899|
|5.2485124653727215|0.49536920319327216| 0.48771393128636853|
+------------------+-------------------+--------------------+
only showing top 5 rows
dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
// importing for regression
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._
val lm = new LinearRegression
lm.explainParams
lm.getStandardization
lm.setStandardization(false)
lm.getStandardization
lm.explainParams
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._
lm: org.apache.spark.ml.regression.LinearRegression = linReg_fd18c4adb9af
res5: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxBlockSizeInMB: Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0. (default: 0.0)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true, current: false)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)
// Transform data frame to required format
val dflr = (dfLR map {row => (row.getDouble(0),
Vectors.dense(row.getDouble(1),row.getDouble(2)))}).
toDF("label","features")
dflr.show(5)
+------------------+--------------------+
| label| features|
+------------------+--------------------+
| 5.677461671611596|[1.69838762332827...|
|6.5963106438672625|[1.93069371189352...|
|-6.457080480280965|[-3.6135790390936...|
| 5.725199404014689|[1.47741286270633...|
|5.2485124653727215|[0.49536920319327...|
+------------------+--------------------+
only showing top 5 rows
dflr: org.apache.spark.sql.DataFrame = [label: double, features: vector]
// Fit model
val fit = lm.fit(dflr)
fit.intercept
fit: org.apache.spark.ml.regression.LinearRegressionModel = LinearRegressionModel: uid=linReg_fd18c4adb9af, numFeatures=2
res8: Double = 1.4942320912612896
fit.coefficients
res9: org.apache.spark.ml.linalg.Vector = [1.9996093571014764,1.0144075351786204]
val summ = fit.summary
summ: org.apache.spark.ml.regression.LinearRegressionTrainingSummary = org.apache.spark.ml.regression.LinearRegressionTrainingSummary@b34e84f
summ.r2
res10: Double = 0.9436414660418528
summ.rootMeanSquaredError
res11: Double = 1.0026971017612039
summ.coefficientStandardErrors
res12: Array[Double] = Array(0.005018897596822849, 0.009968316877946075, 0.01123197045538135)
summ.pValues
res13: Array[Double] = Array(0.0, 0.0, 0.0)
summ.tValues
res14: Array[Double] = Array(398.4160502432455, 101.76317101464718, 133.03383384038267)
summ.predictions.show(5)
+------------------+--------------------+------------------+
| label| features| prediction|
+------------------+--------------------+------------------+
| 5.677461671611596|[1.69838762332827...| 5.043570003209616|
|6.5963106438672625|[1.93069371189352...| 5.134647336087737|
|-6.457080480280965|[-3.6135790390936...|-6.687105473093168|
| 5.725199404014689|[1.47741286270633...| 3.834711189101326|
|5.2485124653727215|[0.49536920319327...|2.9795176720949392|
+------------------+--------------------+------------------+
only showing top 5 rows
summ.residuals.show(5)
+-------------------+
| residuals|
+-------------------+
| 0.6338916684019802|
| 1.4616633077795251|
|0.23002499281220334|
| 1.890488214913363|
| 2.2689947932777823|
+-------------------+
only showing top 5 rows
This gives you more on doing generalised linear modelling in Scala. But let's go back to out pipeline using the power-plant data.
Exercise: Writing your own Spark program for the least squares fit
Your task is to use the syntactic sugar below to write your own linear regression function using reduce and broadcast operations
How would you write your own Spark program to find the least squares fit on the following 1000 data points in RDD rddLR, where the variable y is the response variable, and X1 and X2 are independent variables?
More precisely, find \(w_1, w_2\), such that,
\(\sum_{i=1}^{10} (w_1 X1_i + w_2 X2_i - y_i)^2\) is minimized.
Report \(w_1\), \(w_2\), and the Root Mean Square Error and submit code in Spark. Analyze the resulting algorithm in terms of all-to-all, one-to-all, and all-to-one communication patterns.
rddLR.count
res17: Long = 10000
rddLR.take(10)
res18: Array[(Double, Double, Double)] = Array((5.677461671611596,1.6983876233282715,0.1510498720110515), (6.5963106438672625,1.9306937118935266,-0.21709023201809416), (-6.457080480280965,-3.613579039093624,-0.9420189342233366), (5.725199404014689,1.4774128627063305,-0.6050521762158899), (5.2485124653727215,0.49536920319327216,0.48771393128636853), (6.147100844408568,2.619266385545484,0.027986752952643124), (-0.7268042607303868,-1.6216138344745752,0.08206698908429294), (5.5363351738375695,1.2511380715559544,-0.7810931162704136), (-0.1828379004896784,-0.6891022233658615,-1.2057806605697985), (6.488199218865876,2.7890847095863527,-0.9176042669882863))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show(10)
+-------------------+-------------------+--------------------+
| y| x1| x2|
+-------------------+-------------------+--------------------+
| 5.677461671611596| 1.6983876233282715| 0.1510498720110515|
| 6.5963106438672625| 1.9306937118935266|-0.21709023201809416|
| -6.457080480280965| -3.613579039093624| -0.9420189342233366|
| 5.725199404014689| 1.4774128627063305| -0.6050521762158899|
| 5.2485124653727215|0.49536920319327216| 0.48771393128636853|
| 6.147100844408568| 2.619266385545484|0.027986752952643124|
|-0.7268042607303868|-1.6216138344745752| 0.08206698908429294|
| 5.5363351738375695| 1.2511380715559544| -0.7810931162704136|
|-0.1828379004896784|-0.6891022233658615| -1.2057806605697985|
| 6.488199218865876| 2.7890847095863527| -0.9176042669882863|
+-------------------+-------------------+--------------------+
only showing top 10 rows
dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
rddLR.getNumPartitions
res21: Int = 2
rddLR.map( yx1x2 => (yx1x2._2, yx1x2._3, yx1x2._1) ).take(10)
res22: Array[(Double, Double, Double)] = Array((1.6983876233282715,0.1510498720110515,5.677461671611596), (1.9306937118935266,-0.21709023201809416,6.5963106438672625), (-3.613579039093624,-0.9420189342233366,-6.457080480280965), (1.4774128627063305,-0.6050521762158899,5.725199404014689), (0.49536920319327216,0.48771393128636853,5.2485124653727215), (2.619266385545484,0.027986752952643124,6.147100844408568), (-1.6216138344745752,0.08206698908429294,-0.7268042607303868), (1.2511380715559544,-0.7810931162704136,5.5363351738375695), (-0.6891022233658615,-1.2057806605697985,-0.1828379004896784), (2.7890847095863527,-0.9176042669882863,6.488199218865876))
import breeze.linalg.DenseVector
val pts = rddLR.map( yx1x2 => DenseVector(yx1x2._2, yx1x2._3, yx1x2._1) ).cache
import breeze.linalg.DenseVector
pts: org.apache.spark.rdd.RDD[breeze.linalg.DenseVector[Double]] = MapPartitionsRDD[40] at map at command-2971213210277150:2
Now all we need to do is one-to-all and all-to-one broadcast of the gradient...
val w = DenseVector(0.0, 0.0, 0.0)
val w_bc = sc.broadcast(w)
val step = 0.1
val max_iter = 100
/*
YouTry: Fix the expressions for grad_w0, grad_w1 and grad_w2 to have w_bc.value be the same as that from ml lib's fit.coefficients
*/
for (i <- 1 to max_iter) {
val grad_w0 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*1.0).reduce(_+_)
val grad_w1 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(0)).reduce(_+_)
val grad_w2 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(1)).reduce(_+_)
w_bc.value(0) = w_bc.value(0) - step*grad_w0
w_bc.value(1) = w_bc.value(1) - step*grad_w1
w_bc.value(2) = w_bc.value(2) - step*grad_w2
}
w: breeze.linalg.DenseVector[Double] = DenseVector(703602.5501372493, 2297216.693185762, 194396.78259181185)
w_bc: org.apache.spark.broadcast.Broadcast[breeze.linalg.DenseVector[Double]] = Broadcast(16)
step: Double = 0.1
max_iter: Int = 100
w_bc.value
res24: breeze.linalg.DenseVector[Double] = DenseVector(703602.5501372493, 2297216.693185762, 194396.78259181185)
fit.intercept
res25: Double = 1.4942320912612896
fit.coefficients
res26: org.apache.spark.ml.linalg.Vector = [1.9996093571014764,1.0144075351786204]
Computing each of the gradients requires an all-to-one communication (due to the .reduce(_+_)). There are two of these per iteration. Broadcasting the updated w_bc requires one to all communication.
A more detailed deep dive
We will mostly be using ML algorithms already implemented in Spark's libraries and packages.
However, in order to innovate and devise new algorithms, we need to understand more about distributed linear algebra and optimisation algorithms.
- read: http://arxiv.org/pdf/1509.02256.pdf (also see References and Appendix A).
- and go through the notebooks prepended by '19x' on various Data Types for distributed linear algebra.
You may want to follow this course from Stanford for a guided deeper dive. * http://stanford.edu/~rezab/dao/ * see notes here: https://github.com/lamastex/scalable-data-science/blob/master/read/daosu.pdf
Communication Hierarchy
In addition to time and space complexity of an algorithm, in the distributed setting, we also need to take care of communication complexity.
Access rates fall sharply with distance.
- roughly 50 x gap between reading from memory and reading from either disk or the network.
We must take this communication hierarchy into consideration when developing parallel and distributed algorithms.
Focusing on strategies to reduce communication costs.
- access rates fall sharply with distance.
- so this communication hierarchy needs to be accounted for when developing parallel and distributed algorithms.
Lessons:
-
parallelism makes our computation faster
-
but network communication slows us down
-
SOLUTION: perform parallel and in-memory computation.
-
Persisting in memory is a particularly attractive option when working with iterative algorithms that read the same data multiple times, as is the case in gradient descent.
-
Several machine learning algorithms are iterative!
-
Limits of multi-core scaling (powerful multicore machine with several CPUs, and a huge amount of RAM).
- advantageous:
- sidestep any network communication when working with a single multicore machine
- can indeed handle fairly large data sets, and they're an attractive option in many settings.
- disadvantages:
- can be quite expensive (due to specialized hardware),
- not as widely accessible as commodity computing nodes.
- this approach does have scalability limitations, as we'll eventually hit a wall when the data grows large enough! This is not the case for a distributed environment (like the AWS EC2 cloud under the hood here).
- advantageous:
Simple strategies for algorithms in a distributed setting: to reduce network communication, simply keep large objects local
- In the big n, small d case for linear regression
- we can solve the problem via a closed form solution.
- And this requires us to communicate \(O(d)^2\) intermediate data.
- the largest object in this example is our initial data, which we store in a distributed fashion and never communicate! This is a data parallel setting.
- In the big n, big d case (n is the sample size and d is the number of features):
- for linear regression.
- we use gradient descent to iteratively train our model and are again in a data parallel setting.
- At each iteration we communicate the current parameter vector \(w_i\) and the required \(O(d)\) communication is feasible even for fairly large d.
- for linear regression.
- In the small n, small d case:
- for ridge regression.
- we can communicate the small data to all of the workers.
- this is an example of a model parallel setting where we can train the model for each hyper-parameter in parallel.
- for ridge regression.
- Linear regression with big n and huge d is an example of both data and model parallelism.
In this setting, since our data is large, we must still store it across multiple machines. We can still use gradient descent, or stochastic variants of gradient descent to train our model, but we may not want to communicate the entire d dimensional parameter vector at each iteration, when we have 10s, or hundreds of millions of features. In this setting we often rely on sparsity to reduce the communication. So far we discussed how we can reduce communication by keeping large data local.
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Why are we going through the basic distributed linear algebra Data Types?
This is to get you to be able to directly use them in a project or to roll your own algorithms.
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Local vector in Scala
A local vector has integer-typed and 0-based indices and double-typed values, stored on a single machine.
MLlib supports two types of local vectors:
- dense and
- sparse.
A dense vector is backed by a double array representing its entry values, while a sparse vector is backed by two parallel arrays: indices and values.
For example, a vector (1.0, 0.0, 3.0) can be represented:
- in dense format as
[1.0, 0.0, 3.0]or - in sparse format as
(3, [0, 2], [1.0, 3.0]), where3is the size of the vector.
The base class of local vectors is Vector, and we provide two implementations: DenseVector and SparseVector. We recommend using the factory methods implemented in Vectors to create local vectors. Refer to the Vector Scala docs and Vectors Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
// Create a dense vector (1.0, 0.0, 3.0).
val dv: Vector = Vectors.dense(1.0, 0.0, 3.0)
import org.apache.spark.mllib.linalg.{Vector, Vectors}
dv: org.apache.spark.mllib.linalg.Vector = [1.0,0.0,3.0]
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.
val sv1: Vector = Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0))
sv1: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries.
val sv2: Vector = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0)))
sv2: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])
Note: Scala imports scala.collection.immutable.Vector by default, so you have to import org.apache.spark.mllib.linalg.Vector explicitly to use MLlib’s Vector.
python: MLlib recognizes the following types as dense vectors:
- NumPy’s
array - Python’s list, e.g.,
[1, 2, 3]
and the following as sparse vectors:
- MLlib’s
SparseVector. - SciPy’s
csc_matrixwith a single column
We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented in Vectors to create sparse vectors.
Refer to the Vectors Python docs for more details on the API.
import numpy as np
import scipy.sparse as sps
from pyspark.mllib.linalg import Vectors
# Use a NumPy array as a dense vector.
dv1 = np.array([1.0, 0.0, 3.0])
# Use a Python list as a dense vector.
dv2 = [1.0, 0.0, 3.0]
# Create a SparseVector.
sv1 = Vectors.sparse(3, [0, 2], [1.0, 3.0])
# Use a single-column SciPy csc_matrix as a sparse vector.
sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape = (3, 1))
print (dv1)
print (dv2)
print (sv1)
print (sv2)
[1. 0. 3.]
[1.0, 0.0, 3.0]
(3,[0,2],[1.0,3.0])
(0, 0) 1.0
(2, 0) 3.0
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Labeled point in Scala
A labeled point is a local vector, either dense or sparse, associated with a label/response. In MLlib, labeled points are used in supervised learning algorithms.
We use a double to store a label, so we can use labeled points in both regression and classification.
For binary classification, a label should be either 0 (negative) or 1 (positive). For multiclass classification, labels should be class indices starting from zero: 0, 1, 2, ....
A labeled point is represented by the case class LabeledPoint.
Refer to the LabeledPoint Scala docs for details on the API.
//import first
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
// Create a labeled point with a "positive" label and a dense feature vector.
val pos = LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0))
pos: org.apache.spark.mllib.regression.LabeledPoint = (1.0,[1.0,0.0,3.0])
// Create a labeled point with a "negative" label and a sparse feature vector.
val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
neg: org.apache.spark.mllib.regression.LabeledPoint = (0.0,(3,[0,2],[1.0,3.0]))
Sparse data in Scala
It is very common in practice to have sparse training data. MLlib supports reading training examples stored in LIBSVM format, which is the default format used by LIBSVM and LIBLINEAR. It is a text format in which each line represents a labeled sparse feature vector using the following format:
label index1:value1 index2:value2 ...
where the indices are one-based and in ascending order. After loading, the feature indices are converted to zero-based.
MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.
Refer to the MLUtils Scala docs for details on the API.
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
//val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") // from prog guide but no such data here - can wget from github
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
Load MNIST training and test datasets
Our datasets are vectors of pixels representing images of handwritten digits. For example:

display(dbutils.fs.ls("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt"))
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt | mnist-digits-train.txt | 6.9430283e7 |
val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
examples: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[3661] at map at MLUtils.scala:88
examples.take(1)
res1: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))
Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).
display(examples.toDF) // covert to DataFrame and display for convenient db visualization
The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) below, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):
type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]
Here
type: 0says we hve a sparse vector.size: 780says the vector has 780 indices in total- these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
indices: [152,153,155,...,682,683]are the indices from the[0,1,...,779]possible indices with non-zero values- a value is an integer encoding the gray-level at the pixel index
values: [3, 18, 18,18,126,...,132,16]are the actual gray level values, for example:- at pixed index
152the gray-level value is3, - at index
153the gray-level value is18, - ..., and finally at
- at index
683the gray-level value is18
- at pixed index
We could also use the following method as done in notebook 016_* already.
val training = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
display(training)
Labeled point in Python
A labeled point is represented by LabeledPoint.
Refer to the LabeledPoint Python docs for more details on the API.
# import first
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint
# Create a labeled point with a positive label and a dense feature vector.
pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])
# Create a labeled point with a negative label and a sparse feature vector.
neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))
Sparse data in Python
MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.
Refer to the MLUtils Python docs for more details on the API.
from pyspark.mllib.util import MLUtils
# examples = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") #from prog guide but no such data here - can wget from github
examples = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
examples.take(1)
res5: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Local Matrix in Scala
A local matrix has integer-typed row and column indices and double-typed values, stored on a single machine. MLlib supports:
- dense matrices, whose entry values are stored in a single double array in column-major order, and
- sparse matrices, whose non-zero entry values are stored in the Compressed Sparse Column (CSC) format in column-major order.
For example, the following dense matrix: \[ \begin{pmatrix} 1.0 & 2.0 \\ 3.0 & 4.0 \\ 5.0 & 6.0 \end{pmatrix} \] is stored in a one-dimensional array [1.0, 3.0, 5.0, 2.0, 4.0, 6.0] with the matrix size (3, 2).
The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.
Refer to the Matrix Scala docs and Matrices Scala docs for details on the API.
Int.MaxValue // note the largest value an index can take
res0: Int = 2147483647
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
dm: org.apache.spark.mllib.linalg.Matrix =
1.0 2.0
3.0 4.0
5.0 6.0
Next, let us create the following sparse local matrix: \[ \begin{pmatrix} 9.0 & 0.0 \\ 0.0 & 8.0 \\ 0.0 & 6.0 \end{pmatrix} \]
// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8))
sm: org.apache.spark.mllib.linalg.Matrix =
3 x 2 CSCMatrix
(0,0) 9.0
(2,1) 6.0
(1,1) 8.0
Local Matrix in Python
The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.
Refer to the Matrix Python docs and Matrices Python docs for more details on the API.
from pyspark.mllib.linalg import Matrix, Matrices
# Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
dm2 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])
dm2
# Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8])
sm
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Distributed matrix in Scala
A distributed matrix has long-typed row and column indices and double-typed values, stored distributively in one or more RDDs.
It is very important to choose the right format to store large and distributed matrices. Converting a distributed matrix to a different format may require a global shuffle, which is quite expensive.
Three types of distributed matrices have been implemented so far.
- The basic type is called
RowMatrix.
- A
RowMatrixis a row-oriented distributed matrix without meaningful row indices, e.g., a collection of feature vectors. It is backed by an RDD of its rows, where each row is a local vector. - We assume that the number of columns is not huge for a
RowMatrixso that a single local vector can be reasonably communicated to the driver and can also be stored / operated on using a single node. - An
IndexedRowMatrixis similar to aRowMatrixbut with row indices, which can be used for identifying rows and executing joins. - A
CoordinateMatrixis a distributed matrix stored in coordinate list (COO) format, backed by an RDD of its entries.
Note
The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. In general the use of non-deterministic RDDs can lead to errors.
Remark: there is a huge difference in the orders of magnitude between the maximum size of local versus distributed matrices!
print(Long.MaxValue.toDouble, Int.MaxValue.toDouble, Long.MaxValue.toDouble / Int.MaxValue.toDouble) // index ranges and ratio for local and distributed matrices
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
RowMatrix in Scala
A RowMatrix is a row-oriented distributed matrix without meaningful row indices, backed by an RDD of its rows, where each row is a local vector. Since each row is represented by a local vector, the number of columns is limited by the integer range but it should be much smaller in practice.
A RowMatrix can be created from an RDD[Vector] instance. Then we can compute its column summary statistics and decompositions.
- QR decomposition is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix.
- For singular value decomposition (SVD) and principal component analysis (PCA), please refer to Dimensionality reduction.
Refer to the RowMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
val rows: RDD[Vector] = sc.parallelize(Array(Vectors.dense(12.0, -51.0, 4.0), Vectors.dense(6.0, 167.0, -68.0), Vectors.dense(-4.0, 24.0, -41.0))) // an RDD of local vectors
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector] = ParallelCollectionRDD[3670] at parallelize at command-2972105651606776:1
// Create a RowMatrix from an RDD[Vector].
val mat: RowMatrix = new RowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@75c66624
mat.rows.collect
res0: Array[org.apache.spark.mllib.linalg.Vector] = Array([12.0,-51.0,4.0], [6.0,167.0,-68.0], [-4.0,24.0,-41.0])
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 3
n: Long = 3
// QR decomposition
val qrResult = mat.tallSkinnyQR(true)
qrResult: org.apache.spark.mllib.linalg.QRDecomposition[org.apache.spark.mllib.linalg.distributed.RowMatrix,org.apache.spark.mllib.linalg.Matrix] =
QRDecomposition(org.apache.spark.mllib.linalg.distributed.RowMatrix@34f5da4f,14.0 21.0 -14.0
0.0 -174.99999999999997 70.00000000000001
0.0 0.0 -35.000000000000014 )
qrResult.R
res1: org.apache.spark.mllib.linalg.Matrix =
14.0 21.0 -14.0
0.0 -174.99999999999997 70.00000000000001
0.0 0.0 -35.000000000000014
RowMatrix in Python
A RowMatrix can be created from an RDD of vectors.
Refer to the RowMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import RowMatrix
# Create an RDD of vectors.
rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
# Create a RowMatrix from an RDD of vectors.
mat = RowMatrix(rows)
# Get its size.
m = mat.numRows() # 4
n = mat.numCols() # 3
print (m,'x',n)
# Get the rows as an RDD of vectors again.
rowsRDD = mat.rows
4 x 3
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
IndexedRowMatrix in Scala
An IndexedRowMatrix is similar to a RowMatrix but with meaningful row indices. It is backed by an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local vector.
An IndexedRowMatrix can be created from an RDD[IndexedRow] instance, where IndexedRow is a wrapper over (Long, Vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.
Refer to the IndexedRowMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
Vector(12.0, -51.0, 4.0) // note Vector is a scala collection
res0: scala.collection.immutable.Vector[Double] = Vector(12.0, -51.0, 4.0)
Vectors.dense(12.0, -51.0, 4.0) // while this is a mllib.linalg.Vector
res1: org.apache.spark.mllib.linalg.Vector = [12.0,-51.0,4.0]
val rows: RDD[IndexedRow] = sc.parallelize(Array(IndexedRow(2, Vectors.dense(1,3)), IndexedRow(4, Vectors.dense(4,5)))) // an RDD of indexed rows
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.IndexedRow] = ParallelCollectionRDD[3685] at parallelize at command-2972105651607186:1
// Create an IndexedRowMatrix from an RDD[IndexedRow].
val mat: IndexedRowMatrix = new IndexedRowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@300e1e99
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 5
n: Long = 2
// Drop its row indices.
val rowMat: RowMatrix = mat.toRowMatrix()
rowMat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@425fc4a2
rowMat.rows.collect()
res2: Array[org.apache.spark.mllib.linalg.Vector] = Array([1.0,3.0], [4.0,5.0])
IndexedRowMatrix in Python
An IndexedRowMatrix can be created from an RDD of IndexedRows, where IndexedRow is a wrapper over (long, vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.
Refer to the IndexedRowMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
# Create an RDD of indexed rows.
# - This can be done explicitly with the IndexedRow class:
indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
IndexedRow(1, [4, 5, 6]),
IndexedRow(2, [7, 8, 9]),
IndexedRow(3, [10, 11, 12])])
# - or by using (long, vector) tuples:
indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]),
(2, [7, 8, 9]), (3, [10, 11, 12])])
# Create an IndexedRowMatrix from an RDD of IndexedRows.
mat = IndexedRowMatrix(indexedRows)
# Get its size.
m = mat.numRows() # 4
n = mat.numCols() # 3
print (m,n)
# Get the rows as an RDD of IndexedRows.
rowsRDD = mat.rows
# Convert to a RowMatrix by dropping the row indices.
rowMat = mat.toRowMatrix()
# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()
# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
4 3
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
CoordinateMatrix in Scala
A CoordinateMatrix is a distributed matrix backed by an RDD of its entries. Each entry is a tuple of (i: Long, j: Long, value: Double), where i is the row index, j is the column index, and value is the entry value. A CoordinateMatrix should be used only when both dimensions of the matrix are huge and the matrix is very sparse.
A CoordinateMatrix can be created from an RDD[MatrixEntry] instance, where MatrixEntry is a wrapper over (Long, Long, Double). A CoordinateMatrix can be converted to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix. Other computations for CoordinateMatrix are not currently supported.
Refer to the CoordinateMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3712] at parallelize at command-2972105651606627:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val mat: CoordinateMatrix = new CoordinateMatrix(entries)
mat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@56954954
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 7
n: Long = 2
// Convert it to an IndexRowMatrix whose rows are sparse vectors.
val indexedRowMatrix = mat.toIndexedRowMatrix()
indexedRowMatrix: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@45570b82
indexedRowMatrix.rows.collect()
res0: Array[org.apache.spark.mllib.linalg.distributed.IndexedRow] = Array(IndexedRow(0,(2,[0],[1.2])), IndexedRow(1,(2,[0],[2.1])), IndexedRow(6,(2,[1],[3.7])))
CoordinateMatrix in Scala
A CoordinateMatrix can be created from an RDD of MatrixEntry entries, where MatrixEntry is a wrapper over (long, long, float). A CoordinateMatrix can be converted to a RowMatrix by calling toRowMatrix, or to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix.
Refer to the CoordinateMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry
# Create an RDD of coordinate entries.
# - This can be done explicitly with the MatrixEntry class:
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)])
# - or using (long, long, float) tuples:
entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)])
# Create an CoordinateMatrix from an RDD of MatrixEntries.
mat = CoordinateMatrix(entries)
# Get its size.
m = mat.numRows() # 3
n = mat.numCols() # 2
print (m,n)
# Get the entries as an RDD of MatrixEntries.
entriesRDD = mat.entries
# Convert to a RowMatrix.
rowMat = mat.toRowMatrix()
# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()
# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
3 2
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
BlockMatrix in Scala
A BlockMatrix is a distributed matrix backed by an RDD of MatrixBlocks, where a MatrixBlock is a tuple of ((Int, Int), Matrix), where the (Int, Int) is the index of the block, and Matrix is the sub-matrix at the given index with size rowsPerBlock x colsPerBlock. BlockMatrix supports methods such as add and multiply with another BlockMatrix. BlockMatrix also has a helper function validate which can be used to check whether the BlockMatrix is set up properly.
A BlockMatrix can be most easily created from an IndexedRowMatrix or CoordinateMatrix by calling toBlockMatrix. toBlockMatrix creates blocks of size 1024 x 1024 by default. Users may change the block size by supplying the values through toBlockMatrix(rowsPerBlock, colsPerBlock).
Refer to the BlockMatrix Scala docs for details on the API.
//import org.apache.spark.mllib.linalg.{Matrix, Matrices}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3746] at parallelize at command-2972105651607062:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val coordMat: CoordinateMatrix = new CoordinateMatrix(entries)
coordMat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@161a1942
// Transform the CoordinateMatrix to a BlockMatrix
val matA: BlockMatrix = coordMat.toBlockMatrix().cache()
matA: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@1ddf85ce
// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid.
// Nothing happens if it is valid.
matA.validate()
// Calculate A^T A.
val ata = matA.transpose.multiply(matA)
ata: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@18c0eeca
ata.blocks.collect()
res1: Array[((Int, Int), org.apache.spark.mllib.linalg.Matrix)] =
Array(((0,0),5.85 0.0
0.0 13.690000000000001 ))
ata.toLocalMatrix()
res2: org.apache.spark.mllib.linalg.Matrix =
5.85 0.0
0.0 13.690000000000001
BlockMatrix in Scala
A BlockMatrix can be created from an RDD of sub-matrix blocks, where a sub-matrix block is a ((blockRowIndex, blockColIndex), sub-matrix) tuple.
Refer to the BlockMatrix Python docs for more details on the API.
from pyspark.mllib.linalg import Matrices
from pyspark.mllib.linalg.distributed import BlockMatrix
# Create an RDD of sub-matrix blocks.
blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
# Create a BlockMatrix from an RDD of sub-matrix blocks.
mat = BlockMatrix(blocks, 3, 2)
# Get its size.
m = mat.numRows() # 6
n = mat.numCols() # 2
print (m,n)
# Get the blocks as an RDD of sub-matrix blocks.
blocksRDD = mat.blocks
# Convert to a LocalMatrix.
localMat = mat.toLocalMatrix()
# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()
# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()
6 2
Power Plant ML Pipeline Application
This is an end-to-end example of using a number of different machine learning algorithms to solve a supervised regression problem.
Table of Contents
- Step 1: Business Understanding
- Step 2: Load Your Data
- Step 3: Explore Your Data
- Step 4: Visualize Your Data
- Step 5: Data Preparation
- Step 6: Data Modeling
- Step 7: Tuning and Evaluation
- Step 8: Deployment
We are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant. Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid.
More information about Peaker or Peaking Power Plants can be found on Wikipedia https://en.wikipedia.org/wiki/Peakingpowerplant
Given this business problem, we need to translate it to a Machine Learning task. The ML task is regression since the label (or target) we are trying to predict is numeric.
The example data is provided by UCI at UCI Machine Learning Repository Combined Cycle Power Plant Data Set
You can read the background on the UCI page, but in summary we have collected a number of readings from sensors at a Gas Fired Power Plant
(also called a Peaker Plant) and now we want to use those sensor readings to predict how much power the plant will generate.
More information about Machine Learning with Spark can be found in the Spark MLLib Programming Guide
Please note this example only works with Spark version 1.4 or higher
To Rerun Steps 1-4 done in the notebook at:
Workspace -> PATH_TO -> 009_PowerPlantPipeline_01ETLEDA]
just run the following command as shown in the cell below:
%run "/PATH_TO/009_PowerPlantPipeline_01ETLEDA"
-
Note: If you already evaluated the
%run ...command above then:- first delete the cell by pressing on
xon the top-right corner of the cell and - revaluate the
runcommand above.
- first delete the cell by pressing on
"../009_PowerPlantPipeline_01ETLEDA"
Now we will do the following Steps:
Step 5: Data Preparation,
Step 6: Modeling, and
Step 7: Tuning and Evaluation
We will do Step 8: Deployment later after we get introduced to SparkStreaming.
Step 5: Data Preparation
The next step is to prepare the data. Since all of this data is numeric and consistent, this is a simple task for us today.
We will need to convert the predictor features from columns to Feature Vectors using the org.apache.spark.ml.feature.VectorAssembler
The VectorAssembler will be the first step in building our ML pipeline.
res2: Int = 301
//Let's quickly recall the schema and make sure our table is here now
table("power_plant_table").printSchema
root
|-- AT: double (nullable = true)
|-- V: double (nullable = true)
|-- AP: double (nullable = true)
|-- RH: double (nullable = true)
|-- PE: double (nullable = true)
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/power-plant/data/Sheet1.tsv | Sheet1.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet2.tsv | Sheet2.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet3.tsv | Sheet3.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet4.tsv | Sheet4.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet5.tsv | Sheet5.tsv | 308693.0 |
powerPlantRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/power-plant/data/Sheet1.tsv MapPartitionsRDD[1] at textFile at command-1267216879634554:1
powerPlantDF // make sure we have the DataFrame too
res82: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
powerPlantDF: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
root
|-- AT: double (nullable = true)
|-- V: double (nullable = true)
|-- AP: double (nullable = true)
|-- RH: double (nullable = true)
|-- PE: double (nullable = true)
import org.apache.spark.ml.feature.VectorAssembler
// make a DataFrame called dataset from the table
val dataset = sqlContext.table("power_plant_table")
val vectorizer = new VectorAssembler()
.setInputCols(Array("AT", "V", "AP", "RH"))
.setOutputCol("features")
import org.apache.spark.ml.feature.VectorAssembler
dataset: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
vectorizer: org.apache.spark.ml.feature.VectorAssembler = VectorAssembler: uid=vecAssembler_ea4ed428c7e4, handleInvalid=error, numInputCols=4
res9: Long = 9568
+-----+-----+-------+-----+------+
| AT| V| AP| RH| PE|
+-----+-----+-------+-----+------+
|14.96|41.76|1024.07|73.17|463.26|
|25.18|62.96|1020.04|59.08|444.37|
| 5.11| 39.4|1012.16|92.14|488.56|
|20.86|57.32|1010.24|76.64|446.48|
|10.82| 37.5|1009.23|96.62| 473.9|
|26.27|59.44|1012.23|58.77|443.67|
|15.89|43.96|1014.02|75.24|467.35|
| 9.48|44.71|1019.12|66.43|478.42|
|14.64| 45.0|1021.78|41.25|475.98|
|11.74|43.56|1015.14|70.72| 477.5|
+-----+-----+-------+-----+------+
only showing top 10 rows
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
res12: Long = 9568
+--------+--------------------+-----------+
|database| tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...| false|
| default| sentimentlex_csv| false|
| default| simple_range| false|
| default| social_media_usage| false|
| default|social_media_usag...| false|
+--------+--------------------+-----------+
+------------------------------------------------------------+--------+-----------+---------+-----------+
|name |database|description|tableType|isTemporary|
+------------------------------------------------------------+--------+-----------+---------+-----------+
|power_plant_predictions |default |null |MANAGED |false |
|sentimentlex_csv |default |null |EXTERNAL |false |
|simple_range |default |null |MANAGED |false |
|social_media_usage |default |null |MANAGED |false |
|social_media_usage_table_partitionedbyplatformbucketedbydate|default |null |MANAGED |false |
+------------------------------------------------------------+--------+-----------+---------+-----------+
+-------+---------------------+-------------------------+
|name |description |locationUri |
+-------+---------------------+-------------------------+
|default|Default Hive database|dbfs:/user/hive/warehouse|
+-------+---------------------+-------------------------+
Step 6: Data Modeling
Now let's model our data to predict what the power output will be given a set of sensor readings
Our first model will be based on simple linear regression since we saw some linear patterns in our data based on the scatter plots during the exploration stage.
+--------+--------------------+-----------+
|database| tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...| false|
| default| sentimentlex_csv| false|
| default| simple_range| false|
| default| social_media_usage| false|
| default|social_media_usag...| false|
| | power_plant_table| true|
+--------+--------------------+-----------+
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
| col_name | data_type | comment |
|---|---|---|
| AT | double | null |
| V | double | null |
| AP | double | null |
| RH | double | null |
| PE | double | null |
| summary | AT | V | AP | RH | PE |
|---|---|---|---|---|---|
| count | 9568 | 9568 | 9568 | 9568 | 9568 |
| mean | 19.65123118729102 | 54.30580372073601 | 1013.2590781772603 | 73.30897784280926 | 454.3650094063554 |
| stddev | 7.4524732296110825 | 12.707892998326784 | 5.938783705811581 | 14.600268756728964 | 17.066994999803402 |
| min | 1.81 | 25.36 | 992.89 | 25.56 | 420.26 |
| max | 37.11 | 81.56 | 1033.3 | 100.16 | 495.76 |
| Temperature | Power |
|---|---|
| 14.96 | 463.26 |
| 25.18 | 444.37 |
| 5.11 | 488.56 |
| 20.86 | 446.48 |
| 10.82 | 473.9 |
| 26.27 | 443.67 |
| 15.89 | 467.35 |
| 9.48 | 478.42 |
| 14.64 | 475.98 |
| 11.74 | 477.5 |
| 17.99 | 453.02 |
| 20.14 | 453.99 |
| 24.34 | 440.29 |
| 25.71 | 451.28 |
| 26.19 | 433.99 |
| 21.42 | 462.19 |
| 18.21 | 467.54 |
| 11.04 | 477.2 |
| 14.45 | 459.85 |
| 13.97 | 464.3 |
| 17.76 | 468.27 |
| 5.41 | 495.24 |
| 7.76 | 483.8 |
| 27.23 | 443.61 |
| 27.36 | 436.06 |
| 27.47 | 443.25 |
| 14.6 | 464.16 |
| 7.91 | 475.52 |
| 5.81 | 484.41 |
| 30.53 | 437.89 |
| 23.87 | 445.11 |
| 26.09 | 438.86 |
| 29.27 | 440.98 |
| 27.38 | 436.65 |
| 24.81 | 444.26 |
| 12.75 | 465.86 |
| 24.66 | 444.37 |
| 16.38 | 450.69 |
| 13.91 | 469.02 |
| 23.18 | 448.86 |
| 22.47 | 447.14 |
| 13.39 | 469.18 |
| 9.28 | 482.8 |
| 11.82 | 476.7 |
| 10.27 | 474.99 |
| 22.92 | 444.22 |
| 16.0 | 461.33 |
| 21.22 | 448.06 |
| 13.46 | 474.6 |
| 9.39 | 473.05 |
| 31.07 | 432.06 |
| 12.82 | 467.41 |
| 32.57 | 430.12 |
| 8.11 | 473.62 |
| 13.92 | 471.81 |
| 23.04 | 442.99 |
| 27.31 | 442.77 |
| 5.91 | 491.49 |
| 25.26 | 447.46 |
| 27.97 | 446.11 |
| 26.08 | 442.44 |
| 29.01 | 446.22 |
| 12.18 | 471.49 |
| 13.76 | 463.5 |
| 25.5 | 440.01 |
| 28.26 | 441.03 |
| 21.39 | 452.68 |
| 7.26 | 474.91 |
| 10.54 | 478.77 |
| 27.71 | 434.2 |
| 23.11 | 437.91 |
| 7.51 | 477.61 |
| 26.46 | 431.65 |
| 29.34 | 430.57 |
| 10.32 | 481.09 |
| 22.74 | 445.56 |
| 13.48 | 475.74 |
| 25.52 | 435.12 |
| 21.58 | 446.15 |
| 27.66 | 436.64 |
| 26.96 | 436.69 |
| 12.29 | 468.75 |
| 15.86 | 466.6 |
| 13.87 | 465.48 |
| 24.09 | 441.34 |
| 20.45 | 441.83 |
| 15.07 | 464.7 |
| 32.72 | 437.99 |
| 18.23 | 459.12 |
| 35.56 | 429.69 |
| 18.36 | 459.8 |
| 26.35 | 433.63 |
| 25.92 | 442.84 |
| 8.01 | 485.13 |
| 19.63 | 459.12 |
| 20.02 | 445.31 |
| 10.08 | 480.8 |
| 27.23 | 432.55 |
| 23.37 | 443.86 |
| 18.74 | 449.77 |
| 14.81 | 470.71 |
| 23.1 | 452.17 |
| 10.72 | 478.29 |
| 29.46 | 428.54 |
| 8.1 | 478.27 |
| 27.29 | 439.58 |
| 17.1 | 457.32 |
| 11.49 | 475.51 |
| 23.69 | 439.66 |
| 13.51 | 471.99 |
| 9.64 | 479.81 |
| 25.65 | 434.78 |
| 21.59 | 446.58 |
| 27.98 | 437.76 |
| 18.8 | 459.36 |
| 18.28 | 462.28 |
| 13.55 | 464.33 |
| 22.99 | 444.36 |
| 23.94 | 438.64 |
| 13.74 | 470.49 |
| 21.3 | 455.13 |
| 27.54 | 450.22 |
| 24.81 | 440.43 |
| 4.97 | 482.98 |
| 15.22 | 460.44 |
| 23.88 | 444.97 |
| 33.01 | 433.94 |
| 25.98 | 439.73 |
| 28.18 | 434.48 |
| 21.67 | 442.33 |
| 17.67 | 457.67 |
| 21.37 | 454.66 |
| 28.69 | 432.21 |
| 16.61 | 457.66 |
| 27.91 | 435.21 |
| 20.97 | 448.22 |
| 10.8 | 475.51 |
| 20.61 | 446.53 |
| 25.45 | 441.3 |
| 30.16 | 433.54 |
| 4.99 | 472.52 |
| 10.51 | 474.77 |
| 33.79 | 435.1 |
| 21.34 | 450.74 |
| 23.4 | 442.7 |
| 32.21 | 426.56 |
| 14.26 | 463.71 |
| 27.71 | 447.06 |
| 21.95 | 452.27 |
| 25.76 | 445.78 |
| 23.68 | 438.65 |
| 8.28 | 480.15 |
| 23.44 | 447.19 |
| 25.32 | 443.04 |
| 3.94 | 488.81 |
| 17.3 | 455.75 |
| 18.2 | 455.86 |
| 21.43 | 457.68 |
| 11.16 | 479.11 |
| 30.38 | 432.84 |
| 23.36 | 448.37 |
| 21.69 | 447.06 |
| 23.62 | 443.53 |
| 21.87 | 445.21 |
| 29.25 | 441.7 |
| 20.03 | 450.93 |
| 18.14 | 451.44 |
| 24.23 | 441.29 |
| 18.11 | 458.85 |
| 6.57 | 481.46 |
| 12.56 | 467.19 |
| 13.4 | 461.54 |
| 27.1 | 439.08 |
| 14.28 | 467.22 |
| 16.29 | 468.8 |
| 31.24 | 426.93 |
| 10.57 | 474.65 |
| 13.8 | 468.97 |
| 25.3 | 433.97 |
| 18.06 | 450.53 |
| 25.42 | 444.51 |
| 15.07 | 469.03 |
| 11.75 | 466.56 |
| 20.23 | 457.57 |
| 27.31 | 440.13 |
| 28.57 | 433.24 |
| 17.9 | 452.55 |
| 23.83 | 443.29 |
| 27.92 | 431.76 |
| 17.34 | 454.97 |
| 17.94 | 456.7 |
| 6.4 | 486.03 |
| 11.78 | 472.79 |
| 20.28 | 452.03 |
| 21.04 | 443.41 |
| 25.11 | 441.93 |
| 30.28 | 432.64 |
| 8.14 | 480.25 |
| 16.86 | 466.68 |
| 6.25 | 494.39 |
| 22.35 | 454.72 |
| 17.98 | 448.71 |
| 21.19 | 469.76 |
| 20.94 | 450.71 |
| 24.23 | 444.01 |
| 19.18 | 453.2 |
| 20.88 | 450.87 |
| 23.67 | 441.73 |
| 14.12 | 465.09 |
| 25.23 | 447.28 |
| 6.54 | 491.16 |
| 20.08 | 450.98 |
| 24.67 | 446.3 |
| 27.82 | 436.48 |
| 15.55 | 460.84 |
| 24.26 | 442.56 |
| 13.45 | 467.3 |
| 11.06 | 479.13 |
| 24.91 | 441.15 |
| 22.39 | 445.52 |
| 11.95 | 475.4 |
| 14.85 | 469.3 |
| 10.11 | 463.57 |
| 23.67 | 445.32 |
| 16.14 | 461.03 |
| 15.11 | 466.74 |
| 24.14 | 444.04 |
| 30.08 | 434.01 |
| 14.77 | 465.23 |
| 27.6 | 440.6 |
| 13.89 | 466.74 |
| 26.85 | 433.48 |
| 12.41 | 473.59 |
| 13.08 | 474.81 |
| 18.93 | 454.75 |
| 20.5 | 452.94 |
| 30.72 | 435.83 |
| 7.55 | 482.19 |
| 13.49 | 466.66 |
| 15.62 | 462.59 |
| 24.8 | 447.82 |
| 10.03 | 462.73 |
| 22.43 | 447.98 |
| 14.95 | 462.72 |
| 24.78 | 442.42 |
| 23.2 | 444.69 |
| 14.01 | 466.7 |
| 19.4 | 453.84 |
| 30.15 | 436.92 |
| 6.91 | 486.37 |
| 29.04 | 440.43 |
| 26.02 | 446.82 |
| 5.89 | 484.91 |
| 26.52 | 437.76 |
| 28.53 | 438.91 |
| 16.59 | 464.19 |
| 22.95 | 442.19 |
| 23.96 | 446.86 |
| 17.48 | 457.15 |
| 6.69 | 482.57 |
| 10.25 | 476.03 |
| 28.87 | 428.89 |
| 12.04 | 472.7 |
| 22.58 | 445.6 |
| 15.12 | 464.78 |
| 25.48 | 440.42 |
| 27.87 | 428.41 |
| 23.72 | 438.5 |
| 25.0 | 438.28 |
| 8.42 | 476.29 |
| 22.46 | 448.46 |
| 29.92 | 438.99 |
| 11.68 | 471.8 |
| 14.04 | 471.81 |
| 19.86 | 449.82 |
| 25.99 | 442.14 |
| 23.42 | 441.46 |
| 10.6 | 477.62 |
| 20.97 | 446.76 |
| 14.14 | 472.52 |
| 8.56 | 471.58 |
| 24.86 | 440.85 |
| 29.0 | 431.37 |
| 27.59 | 437.33 |
| 10.45 | 469.22 |
| 8.51 | 471.11 |
| 29.82 | 439.17 |
| 22.56 | 445.33 |
| 11.38 | 473.71 |
| 20.25 | 452.66 |
| 22.42 | 440.99 |
| 14.85 | 467.42 |
| 25.62 | 444.14 |
| 19.85 | 457.17 |
| 13.67 | 467.87 |
| 24.39 | 442.04 |
| 16.07 | 471.36 |
| 11.6 | 460.7 |
| 31.38 | 431.33 |
| 29.91 | 432.6 |
| 19.67 | 447.61 |
| 27.18 | 443.87 |
| 21.39 | 446.87 |
| 10.45 | 465.74 |
| 19.46 | 447.86 |
| 23.55 | 447.65 |
| 23.35 | 437.87 |
| 9.26 | 483.51 |
| 10.3 | 479.65 |
| 20.94 | 455.16 |
| 23.13 | 431.91 |
| 12.77 | 470.68 |
| 28.29 | 429.28 |
| 19.13 | 450.81 |
| 24.44 | 437.73 |
| 20.32 | 460.21 |
| 20.54 | 442.86 |
| 12.16 | 482.99 |
| 28.09 | 440.0 |
| 9.25 | 478.48 |
| 21.75 | 455.28 |
| 23.7 | 436.94 |
| 16.22 | 461.06 |
| 24.75 | 438.28 |
| 10.48 | 472.61 |
| 29.53 | 426.85 |
| 12.59 | 470.18 |
| 23.5 | 455.38 |
| 29.01 | 428.32 |
| 9.75 | 480.35 |
| 19.55 | 455.56 |
| 21.05 | 447.66 |
| 24.72 | 443.06 |
| 21.19 | 452.43 |
| 10.77 | 477.81 |
| 28.68 | 431.66 |
| 29.87 | 431.8 |
| 22.99 | 446.67 |
| 24.66 | 445.26 |
| 32.63 | 425.72 |
| 31.38 | 430.58 |
| 23.87 | 439.86 |
| 25.6 | 441.11 |
| 27.62 | 434.72 |
| 30.1 | 434.01 |
| 12.19 | 475.64 |
| 13.11 | 460.44 |
| 28.29 | 436.4 |
| 13.45 | 461.03 |
| 10.98 | 479.08 |
| 26.48 | 435.76 |
| 13.07 | 460.14 |
| 25.56 | 442.2 |
| 22.68 | 447.69 |
| 28.86 | 431.15 |
| 22.7 | 445.0 |
| 27.89 | 431.59 |
| 13.78 | 467.22 |
| 28.14 | 445.33 |
| 11.8 | 470.57 |
| 10.71 | 473.77 |
| 24.54 | 447.67 |
| 11.54 | 474.29 |
| 29.47 | 437.14 |
| 29.24 | 432.56 |
| 14.51 | 459.14 |
| 22.91 | 446.19 |
| 27.02 | 428.1 |
| 13.49 | 468.46 |
| 30.24 | 435.02 |
| 23.19 | 445.52 |
| 17.73 | 462.69 |
| 18.62 | 455.75 |
| 12.85 | 463.74 |
| 32.33 | 439.79 |
| 25.09 | 443.26 |
| 29.45 | 432.04 |
| 16.91 | 465.86 |
| 14.09 | 465.6 |
| 10.73 | 469.43 |
| 23.2 | 440.75 |
| 8.21 | 481.32 |
| 9.3 | 479.87 |
| 16.97 | 458.59 |
| 23.69 | 438.62 |
| 25.13 | 445.59 |
| 9.86 | 481.87 |
| 11.33 | 475.01 |
| 26.95 | 436.54 |
| 15.0 | 456.63 |
| 20.76 | 451.69 |
| 14.29 | 463.04 |
| 19.74 | 446.1 |
| 26.68 | 438.67 |
| 14.24 | 466.88 |
| 21.98 | 444.6 |
| 22.75 | 440.26 |
| 8.34 | 483.92 |
| 11.8 | 475.19 |
| 8.81 | 479.24 |
| 30.05 | 434.92 |
| 16.01 | 454.16 |
| 21.75 | 447.58 |
| 13.94 | 467.9 |
| 29.25 | 426.29 |
| 22.33 | 447.02 |
| 16.43 | 455.85 |
| 11.5 | 476.46 |
| 23.53 | 437.48 |
| 21.86 | 452.77 |
| 6.17 | 491.54 |
| 30.19 | 438.41 |
| 11.67 | 476.1 |
| 15.34 | 464.58 |
| 11.5 | 467.74 |
| 25.53 | 442.12 |
| 21.27 | 453.34 |
| 28.37 | 425.29 |
| 28.39 | 449.63 |
| 13.78 | 462.88 |
| 14.6 | 464.67 |
| 5.1 | 489.96 |
| 7.0 | 482.38 |
| 26.3 | 437.95 |
| 30.56 | 429.2 |
| 21.09 | 453.34 |
| 28.21 | 442.47 |
| 15.84 | 462.6 |
| 10.03 | 478.79 |
| 20.37 | 456.11 |
| 21.19 | 450.33 |
| 33.73 | 434.83 |
| 29.87 | 433.43 |
| 19.62 | 456.02 |
| 9.93 | 485.23 |
| 9.43 | 473.57 |
| 14.24 | 469.94 |
| 12.97 | 452.07 |
| 7.6 | 475.32 |
| 8.39 | 480.69 |
| 25.41 | 444.01 |
| 18.43 | 465.17 |
| 10.31 | 480.61 |
| 11.29 | 476.04 |
| 22.61 | 441.76 |
| 29.34 | 428.24 |
| 18.87 | 444.77 |
| 13.21 | 463.1 |
| 11.3 | 470.5 |
| 29.23 | 431.0 |
| 27.76 | 430.68 |
| 29.26 | 436.42 |
| 25.72 | 452.33 |
| 23.43 | 440.16 |
| 25.6 | 435.75 |
| 22.3 | 449.74 |
| 27.91 | 430.73 |
| 30.35 | 432.75 |
| 21.78 | 446.79 |
| 7.19 | 486.35 |
| 20.88 | 453.18 |
| 24.19 | 458.31 |
| 9.98 | 480.26 |
| 23.47 | 448.65 |
| 26.35 | 458.41 |
| 29.89 | 435.39 |
| 19.29 | 450.21 |
| 17.48 | 459.59 |
| 25.21 | 445.84 |
| 23.3 | 441.08 |
| 15.42 | 467.33 |
| 21.44 | 444.19 |
| 29.45 | 432.96 |
| 29.69 | 438.09 |
| 15.52 | 467.9 |
| 11.47 | 475.72 |
| 9.77 | 477.51 |
| 22.6 | 435.13 |
| 8.24 | 477.9 |
| 17.01 | 457.26 |
| 19.64 | 467.53 |
| 10.61 | 465.15 |
| 12.04 | 474.28 |
| 29.19 | 444.49 |
| 21.75 | 452.84 |
| 23.66 | 435.38 |
| 27.05 | 433.57 |
| 29.63 | 435.27 |
| 18.2 | 468.49 |
| 32.22 | 433.07 |
| 26.88 | 430.63 |
| 29.05 | 440.74 |
| 8.9 | 474.49 |
| 18.93 | 449.74 |
| 27.49 | 436.73 |
| 23.1 | 434.58 |
| 11.22 | 473.93 |
| 31.97 | 435.99 |
| 13.32 | 466.83 |
| 31.68 | 427.22 |
| 23.69 | 444.07 |
| 13.83 | 469.57 |
| 18.32 | 459.89 |
| 11.05 | 479.59 |
| 22.03 | 440.92 |
| 10.23 | 480.87 |
| 23.92 | 441.9 |
| 29.38 | 430.2 |
| 17.35 | 465.16 |
| 9.81 | 471.32 |
| 4.97 | 485.43 |
| 5.15 | 495.35 |
| 21.54 | 449.12 |
| 7.94 | 480.53 |
| 18.77 | 457.07 |
| 21.69 | 443.67 |
| 10.07 | 477.52 |
| 13.83 | 472.95 |
| 10.45 | 472.54 |
| 11.56 | 469.17 |
| 23.64 | 435.21 |
| 10.48 | 477.78 |
| 13.09 | 475.89 |
| 10.67 | 483.9 |
| 12.57 | 476.2 |
| 14.45 | 462.16 |
| 14.22 | 471.05 |
| 6.97 | 484.71 |
| 20.61 | 446.34 |
| 14.67 | 469.02 |
| 29.06 | 432.12 |
| 14.38 | 467.28 |
| 32.51 | 429.66 |
| 11.79 | 469.49 |
| 8.65 | 485.87 |
| 9.75 | 481.95 |
| 9.11 | 479.03 |
| 23.39 | 434.5 |
| 14.3 | 464.9 |
| 17.49 | 452.71 |
| 31.1 | 429.74 |
| 19.77 | 457.09 |
| 28.61 | 446.77 |
| 13.52 | 460.76 |
| 13.52 | 471.95 |
| 17.57 | 453.29 |
| 28.18 | 441.61 |
| 14.29 | 464.73 |
| 18.12 | 464.68 |
| 31.27 | 430.59 |
| 26.24 | 438.01 |
| 7.44 | 479.08 |
| 29.78 | 436.39 |
| 23.37 | 447.07 |
| 10.62 | 479.91 |
| 5.84 | 489.05 |
| 14.51 | 463.17 |
| 11.31 | 471.26 |
| 11.25 | 480.49 |
| 9.18 | 473.78 |
| 19.82 | 455.5 |
| 24.77 | 446.27 |
| 9.66 | 482.2 |
| 21.96 | 452.48 |
| 18.59 | 464.48 |
| 24.75 | 438.1 |
| 24.37 | 445.6 |
| 29.6 | 442.43 |
| 25.32 | 436.67 |
| 16.15 | 466.56 |
| 15.74 | 457.29 |
| 5.97 | 487.03 |
| 15.84 | 464.93 |
| 14.84 | 466.0 |
| 12.25 | 469.52 |
| 27.38 | 428.88 |
| 8.76 | 474.3 |
| 15.54 | 461.06 |
| 18.71 | 465.57 |
| 13.06 | 467.67 |
| 12.72 | 466.99 |
| 19.83 | 463.72 |
| 27.23 | 443.78 |
| 24.27 | 445.23 |
| 11.8 | 464.43 |
| 6.76 | 484.36 |
| 25.99 | 442.16 |
| 16.3 | 464.11 |
| 16.5 | 462.48 |
| 10.59 | 477.49 |
| 26.05 | 437.04 |
| 19.5 | 457.09 |
| 22.21 | 450.6 |
| 17.86 | 465.78 |
| 29.96 | 427.1 |
| 19.08 | 459.81 |
| 23.59 | 447.36 |
| 3.38 | 488.92 |
| 26.39 | 433.36 |
| 8.99 | 483.35 |
| 10.91 | 469.53 |
| 13.08 | 476.96 |
| 23.95 | 440.75 |
| 15.64 | 462.55 |
| 18.78 | 448.04 |
| 20.65 | 455.24 |
| 4.96 | 494.75 |
| 23.51 | 444.58 |
| 5.99 | 484.82 |
| 23.65 | 442.9 |
| 5.17 | 485.46 |
| 26.38 | 457.81 |
| 6.02 | 481.92 |
| 23.2 | 443.23 |
| 8.57 | 474.29 |
| 30.72 | 430.46 |
| 21.52 | 455.71 |
| 22.93 | 438.34 |
| 5.71 | 485.83 |
| 18.62 | 452.82 |
| 27.88 | 435.04 |
| 22.32 | 451.21 |
| 14.55 | 465.81 |
| 17.83 | 458.42 |
| 9.68 | 470.22 |
| 19.41 | 449.24 |
| 13.22 | 471.43 |
| 12.24 | 473.26 |
| 19.21 | 452.82 |
| 29.74 | 432.69 |
| 23.28 | 444.13 |
| 8.02 | 467.21 |
| 22.47 | 445.98 |
| 27.51 | 436.91 |
| 17.51 | 455.01 |
| 23.22 | 437.11 |
| 11.73 | 477.06 |
| 21.19 | 441.71 |
| 5.48 | 495.76 |
| 24.26 | 445.63 |
| 12.32 | 464.72 |
| 31.26 | 438.03 |
| 32.09 | 434.78 |
| 24.98 | 444.67 |
| 27.48 | 452.24 |
| 21.04 | 450.92 |
| 27.75 | 436.53 |
| 22.79 | 435.53 |
| 24.22 | 440.01 |
| 27.06 | 443.1 |
| 29.25 | 427.49 |
| 26.86 | 436.25 |
| 29.64 | 440.74 |
| 19.92 | 443.54 |
| 18.5 | 459.42 |
| 23.71 | 439.66 |
| 14.39 | 464.15 |
| 19.3 | 459.1 |
| 24.65 | 455.68 |
| 13.5 | 469.08 |
| 9.82 | 478.02 |
| 18.4 | 456.8 |
| 28.12 | 441.13 |
| 17.15 | 463.88 |
| 30.69 | 430.45 |
| 28.82 | 449.18 |
| 21.3 | 447.89 |
| 30.58 | 431.59 |
| 21.17 | 447.5 |
| 9.87 | 475.58 |
| 22.18 | 453.24 |
| 24.39 | 446.4 |
| 10.73 | 476.81 |
| 9.38 | 474.1 |
| 20.27 | 450.71 |
| 24.82 | 433.62 |
| 16.55 | 465.14 |
| 20.73 | 445.18 |
| 9.51 | 474.12 |
| 8.63 | 483.91 |
| 6.48 | 486.68 |
| 14.95 | 464.98 |
| 5.76 | 481.4 |
| 10.94 | 479.2 |
| 15.87 | 463.86 |
| 12.42 | 472.3 |
| 29.12 | 446.51 |
| 29.12 | 437.71 |
| 19.08 | 458.94 |
| 31.06 | 437.91 |
| 5.72 | 490.76 |
| 26.52 | 439.66 |
| 13.84 | 463.27 |
| 13.03 | 473.99 |
| 25.94 | 433.38 |
| 16.64 | 459.01 |
| 14.13 | 471.44 |
| 13.65 | 471.91 |
| 14.5 | 465.15 |
| 19.8 | 446.66 |
| 25.2 | 438.15 |
| 20.66 | 447.14 |
| 12.07 | 472.32 |
| 25.64 | 441.68 |
| 23.33 | 440.04 |
| 29.41 | 444.82 |
| 16.6 | 457.26 |
| 27.53 | 428.83 |
| 20.62 | 449.07 |
| 26.02 | 435.21 |
| 12.75 | 471.03 |
| 12.87 | 465.56 |
| 25.77 | 442.83 |
| 14.84 | 460.3 |
| 7.41 | 474.25 |
| 8.87 | 477.97 |
| 9.69 | 472.16 |
| 16.17 | 456.08 |
| 26.24 | 452.41 |
| 13.78 | 463.71 |
| 26.3 | 433.72 |
| 17.37 | 456.4 |
| 23.6 | 448.43 |
| 8.3 | 481.6 |
| 18.86 | 457.07 |
| 22.12 | 451.0 |
| 28.41 | 440.28 |
| 29.42 | 437.47 |
| 18.61 | 443.57 |
| 27.57 | 426.6 |
| 12.83 | 470.87 |
| 9.64 | 478.37 |
| 19.13 | 453.92 |
| 15.92 | 470.22 |
| 24.64 | 434.54 |
| 27.62 | 442.89 |
| 8.9 | 479.03 |
| 9.55 | 476.06 |
| 10.57 | 473.88 |
| 19.8 | 451.75 |
| 25.63 | 439.2 |
| 24.7 | 439.7 |
| 15.26 | 463.6 |
| 20.06 | 447.47 |
| 19.84 | 447.92 |
| 11.49 | 471.08 |
| 23.74 | 437.55 |
| 22.62 | 448.27 |
| 29.53 | 431.69 |
| 21.32 | 449.09 |
| 20.3 | 448.79 |
| 16.97 | 460.21 |
| 12.07 | 479.28 |
| 7.46 | 483.11 |
| 19.2 | 450.75 |
| 28.64 | 437.97 |
| 13.56 | 459.76 |
| 17.4 | 457.75 |
| 14.08 | 469.33 |
| 27.11 | 433.28 |
| 20.92 | 444.64 |
| 16.18 | 463.1 |
| 15.57 | 460.91 |
| 10.37 | 479.35 |
| 19.6 | 449.23 |
| 9.22 | 474.51 |
| 27.76 | 435.02 |
| 28.68 | 435.45 |
| 20.95 | 452.38 |
| 9.06 | 480.41 |
| 9.21 | 478.96 |
| 13.65 | 468.87 |
| 31.79 | 434.01 |
| 14.32 | 466.36 |
| 26.28 | 435.28 |
| 7.69 | 486.46 |
| 14.44 | 468.19 |
| 9.19 | 468.37 |
| 13.35 | 474.19 |
| 23.04 | 440.32 |
| 4.83 | 485.32 |
| 17.29 | 464.27 |
| 8.73 | 479.25 |
| 26.21 | 430.4 |
| 23.72 | 447.49 |
| 29.27 | 438.23 |
| 10.4 | 492.09 |
| 12.19 | 475.36 |
| 20.4 | 452.56 |
| 34.3 | 427.84 |
| 27.56 | 433.95 |
| 30.9 | 435.27 |
| 14.85 | 454.62 |
| 16.42 | 472.17 |
| 16.45 | 452.42 |
| 10.14 | 472.17 |
| 9.53 | 481.83 |
| 17.01 | 458.78 |
| 23.94 | 447.5 |
| 15.95 | 463.4 |
| 11.15 | 473.57 |
| 25.56 | 433.72 |
| 27.16 | 431.85 |
| 26.71 | 433.47 |
| 29.56 | 432.84 |
| 31.19 | 436.6 |
| 6.86 | 490.23 |
| 12.36 | 477.16 |
| 32.82 | 441.06 |
| 25.3 | 440.86 |
| 8.71 | 477.94 |
| 13.34 | 474.47 |
| 14.2 | 470.67 |
| 23.74 | 447.31 |
| 16.9 | 466.8 |
| 28.54 | 430.91 |
| 30.15 | 434.75 |
| 14.33 | 469.52 |
| 25.57 | 438.9 |
| 30.55 | 429.56 |
| 28.04 | 432.92 |
| 26.39 | 442.87 |
| 15.3 | 466.59 |
| 6.03 | 479.61 |
| 13.49 | 471.08 |
| 27.67 | 433.37 |
| 24.19 | 443.92 |
| 24.44 | 443.5 |
| 29.86 | 439.89 |
| 30.2 | 434.66 |
| 7.99 | 487.57 |
| 9.93 | 464.64 |
| 11.03 | 470.92 |
| 22.34 | 444.39 |
| 25.33 | 442.48 |
| 18.87 | 449.61 |
| 25.97 | 435.02 |
| 16.58 | 458.67 |
| 14.35 | 461.74 |
| 25.06 | 438.31 |
| 13.85 | 462.38 |
| 16.09 | 460.56 |
| 26.34 | 439.22 |
| 23.01 | 444.64 |
| 26.39 | 430.34 |
| 31.32 | 430.46 |
| 16.64 | 456.79 |
| 13.42 | 468.82 |
| 20.06 | 448.51 |
| 14.8 | 470.77 |
| 12.59 | 465.74 |
| 26.7 | 430.21 |
| 19.78 | 449.23 |
| 15.17 | 461.89 |
| 21.71 | 445.72 |
| 19.09 | 466.13 |
| 19.76 | 448.71 |
| 14.68 | 469.25 |
| 21.3 | 450.56 |
| 16.73 | 464.46 |
| 12.26 | 471.13 |
| 14.77 | 461.52 |
| 18.26 | 451.09 |
| 27.1 | 431.51 |
| 14.72 | 469.8 |
| 26.3 | 442.28 |
| 16.48 | 458.67 |
| 17.99 | 462.4 |
| 20.34 | 453.54 |
| 25.53 | 444.38 |
| 31.59 | 440.52 |
| 30.8 | 433.62 |
| 10.75 | 481.96 |
| 19.3 | 452.75 |
| 4.71 | 481.28 |
| 23.1 | 439.03 |
| 32.63 | 435.75 |
| 26.63 | 436.03 |
| 24.35 | 445.6 |
| 15.11 | 462.65 |
| 29.1 | 438.66 |
| 21.24 | 447.32 |
| 6.16 | 484.55 |
| 7.36 | 476.8 |
| 10.44 | 480.34 |
| 26.76 | 440.63 |
| 16.79 | 459.48 |
| 10.76 | 490.78 |
| 6.07 | 483.56 |
| 27.33 | 429.38 |
| 27.15 | 440.27 |
| 22.35 | 445.34 |
| 21.82 | 447.43 |
| 21.11 | 439.91 |
| 19.95 | 459.27 |
| 7.45 | 478.89 |
| 15.36 | 466.7 |
| 15.65 | 463.5 |
| 25.31 | 436.21 |
| 25.88 | 443.94 |
| 24.6 | 439.63 |
| 22.58 | 460.95 |
| 19.69 | 448.69 |
| 25.85 | 444.63 |
| 10.06 | 473.51 |
| 18.59 | 462.56 |
| 18.27 | 451.76 |
| 8.85 | 491.81 |
| 30.04 | 429.52 |
| 26.06 | 437.9 |
| 14.8 | 467.54 |
| 23.93 | 449.97 |
| 23.72 | 436.62 |
| 11.44 | 477.68 |
| 20.28 | 447.26 |
| 27.9 | 439.76 |
| 24.74 | 437.49 |
| 14.8 | 455.14 |
| 8.22 | 485.5 |
| 27.56 | 444.1 |
| 32.07 | 432.33 |
| 9.53 | 471.23 |
| 13.61 | 463.89 |
| 22.2 | 445.54 |
| 21.36 | 446.09 |
| 23.25 | 445.12 |
| 23.5 | 443.31 |
| 8.46 | 484.16 |
| 8.19 | 477.76 |
| 30.67 | 430.28 |
| 32.48 | 446.48 |
| 8.99 | 481.03 |
| 13.77 | 466.07 |
| 19.05 | 447.47 |
| 21.19 | 455.93 |
| 10.12 | 479.62 |
| 24.93 | 455.06 |
| 8.47 | 475.06 |
| 24.52 | 438.89 |
| 28.55 | 432.7 |
| 20.58 | 452.6 |
| 18.31 | 451.75 |
| 27.18 | 430.66 |
| 4.43 | 491.9 |
| 26.02 | 439.82 |
| 15.75 | 460.73 |
| 22.99 | 449.7 |
| 25.52 | 439.42 |
| 27.04 | 439.84 |
| 6.42 | 485.86 |
| 17.04 | 458.1 |
| 10.79 | 479.92 |
| 20.41 | 458.29 |
| 7.36 | 489.45 |
| 28.08 | 434.0 |
| 24.74 | 431.24 |
| 28.32 | 439.5 |
| 16.71 | 467.46 |
| 30.7 | 429.27 |
| 18.42 | 452.1 |
| 10.62 | 472.41 |
| 22.18 | 442.14 |
| 22.38 | 441.0 |
| 13.94 | 463.07 |
| 21.24 | 445.71 |
| 6.76 | 483.16 |
| 26.73 | 440.45 |
| 7.24 | 481.83 |
| 10.84 | 467.6 |
| 19.32 | 450.88 |
| 29.0 | 425.5 |
| 23.38 | 451.87 |
| 31.17 | 428.94 |
| 26.17 | 439.86 |
| 30.9 | 433.44 |
| 24.92 | 438.23 |
| 32.77 | 436.95 |
| 14.37 | 470.19 |
| 8.36 | 484.66 |
| 31.45 | 430.81 |
| 31.6 | 433.37 |
| 17.9 | 453.02 |
| 20.35 | 453.5 |
| 16.21 | 463.09 |
| 19.36 | 464.56 |
| 21.04 | 452.12 |
| 14.05 | 470.9 |
| 23.48 | 450.89 |
| 21.91 | 445.04 |
| 24.42 | 444.72 |
| 14.26 | 460.38 |
| 21.38 | 446.8 |
| 15.71 | 465.05 |
| 5.78 | 484.13 |
| 6.77 | 488.27 |
| 23.84 | 447.09 |
| 21.17 | 452.02 |
| 19.94 | 455.55 |
| 8.73 | 480.99 |
| 16.39 | 467.68 |
| ExhaustVaccum | Power |
|---|---|
| 41.76 | 463.26 |
| 62.96 | 444.37 |
| 39.4 | 488.56 |
| 57.32 | 446.48 |
| 37.5 | 473.9 |
| 59.44 | 443.67 |
| 43.96 | 467.35 |
| 44.71 | 478.42 |
| 45.0 | 475.98 |
| 43.56 | 477.5 |
| 43.72 | 453.02 |
| 46.93 | 453.99 |
| 73.5 | 440.29 |
| 58.59 | 451.28 |
| 69.34 | 433.99 |
| 43.79 | 462.19 |
| 45.0 | 467.54 |
| 41.74 | 477.2 |
| 52.75 | 459.85 |
| 38.47 | 464.3 |
| 42.42 | 468.27 |
| 40.07 | 495.24 |
| 42.28 | 483.8 |
| 63.9 | 443.61 |
| 48.6 | 436.06 |
| 70.72 | 443.25 |
| 39.31 | 464.16 |
| 39.96 | 475.52 |
| 35.79 | 484.41 |
| 65.18 | 437.89 |
| 63.94 | 445.11 |
| 58.41 | 438.86 |
| 66.85 | 440.98 |
| 74.16 | 436.65 |
| 63.94 | 444.26 |
| 44.03 | 465.86 |
| 63.73 | 444.37 |
| 47.45 | 450.69 |
| 39.35 | 469.02 |
| 51.3 | 448.86 |
| 47.45 | 447.14 |
| 44.85 | 469.18 |
| 41.54 | 482.8 |
| 42.86 | 476.7 |
| 40.64 | 474.99 |
| 63.94 | 444.22 |
| 37.87 | 461.33 |
| 43.43 | 448.06 |
| 44.71 | 474.6 |
| 40.11 | 473.05 |
| 73.5 | 432.06 |
| 38.62 | 467.41 |
| 78.92 | 430.12 |
| 42.18 | 473.62 |
| 39.39 | 471.81 |
| 59.43 | 442.99 |
| 64.44 | 442.77 |
| 39.33 | 491.49 |
| 61.08 | 447.46 |
| 58.84 | 446.11 |
| 52.3 | 442.44 |
| 65.71 | 446.22 |
| 40.1 | 471.49 |
| 45.87 | 463.5 |
| 58.79 | 440.01 |
| 65.34 | 441.03 |
| 62.96 | 452.68 |
| 40.69 | 474.91 |
| 34.03 | 478.77 |
| 74.34 | 434.2 |
| 68.3 | 437.91 |
| 41.01 | 477.61 |
| 74.67 | 431.65 |
| 74.34 | 430.57 |
| 42.28 | 481.09 |
| 61.02 | 445.56 |
| 39.85 | 475.74 |
| 69.75 | 435.12 |
| 67.25 | 446.15 |
| 76.86 | 436.64 |
| 69.45 | 436.69 |
| 42.18 | 468.75 |
| 43.02 | 466.6 |
| 45.08 | 465.48 |
| 73.68 | 441.34 |
| 69.45 | 441.83 |
| 39.3 | 464.7 |
| 69.75 | 437.99 |
| 58.96 | 459.12 |
| 68.94 | 429.69 |
| 51.43 | 459.8 |
| 64.05 | 433.63 |
| 60.95 | 442.84 |
| 41.66 | 485.13 |
| 52.72 | 459.12 |
| 67.32 | 445.31 |
| 40.72 | 480.8 |
| 66.48 | 432.55 |
| 63.77 | 443.86 |
| 59.21 | 449.77 |
| 43.69 | 470.71 |
| 51.3 | 452.17 |
| 41.38 | 478.29 |
| 71.94 | 428.54 |
| 40.64 | 478.27 |
| 62.66 | 439.58 |
| 49.69 | 457.32 |
| 44.2 | 475.51 |
| 65.59 | 439.66 |
| 40.89 | 471.99 |
| 39.35 | 479.81 |
| 78.92 | 434.78 |
| 61.87 | 446.58 |
| 58.33 | 437.76 |
| 39.72 | 459.36 |
| 44.71 | 462.28 |
| 43.48 | 464.33 |
| 46.21 | 444.36 |
| 59.39 | 438.64 |
| 34.03 | 470.49 |
| 41.1 | 455.13 |
| 66.93 | 450.22 |
| 63.73 | 440.43 |
| 42.85 | 482.98 |
| 50.88 | 460.44 |
| 54.2 | 444.97 |
| 68.67 | 433.94 |
| 73.18 | 439.73 |
| 73.88 | 434.48 |
| 60.84 | 442.33 |
| 45.09 | 457.67 |
| 57.76 | 454.66 |
| 67.25 | 432.21 |
| 43.77 | 457.66 |
| 63.76 | 435.21 |
| 47.43 | 448.22 |
| 41.66 | 475.51 |
| 62.91 | 446.53 |
| 57.32 | 441.3 |
| 69.34 | 433.54 |
| 39.04 | 472.52 |
| 44.78 | 474.77 |
| 69.05 | 435.1 |
| 59.8 | 450.74 |
| 65.06 | 442.7 |
| 68.14 | 426.56 |
| 42.32 | 463.71 |
| 66.93 | 447.06 |
| 57.76 | 452.27 |
| 63.94 | 445.78 |
| 68.3 | 438.65 |
| 40.77 | 480.15 |
| 62.52 | 447.19 |
| 48.41 | 443.04 |
| 39.9 | 488.81 |
| 57.76 | 455.75 |
| 49.39 | 455.86 |
| 46.97 | 457.68 |
| 40.05 | 479.11 |
| 74.16 | 432.84 |
| 62.52 | 448.37 |
| 47.45 | 447.06 |
| 49.21 | 443.53 |
| 61.45 | 445.21 |
| 66.51 | 441.7 |
| 66.86 | 450.93 |
| 49.78 | 451.44 |
| 56.89 | 441.29 |
| 44.85 | 458.85 |
| 43.65 | 481.46 |
| 43.41 | 467.19 |
| 41.58 | 461.54 |
| 52.84 | 439.08 |
| 42.74 | 467.22 |
| 44.34 | 468.8 |
| 71.98 | 426.93 |
| 37.73 | 474.65 |
| 44.21 | 468.97 |
| 71.58 | 433.97 |
| 50.16 | 450.53 |
| 59.04 | 444.51 |
| 40.69 | 469.03 |
| 71.14 | 466.56 |
| 52.05 | 457.57 |
| 59.54 | 440.13 |
| 69.84 | 433.24 |
| 43.72 | 452.55 |
| 71.37 | 443.29 |
| 74.99 | 431.76 |
| 44.78 | 454.97 |
| 63.07 | 456.7 |
| 39.9 | 486.03 |
| 39.96 | 472.79 |
| 57.25 | 452.03 |
| 54.2 | 443.41 |
| 67.32 | 441.93 |
| 70.98 | 432.64 |
| 36.24 | 480.25 |
| 39.63 | 466.68 |
| 40.07 | 494.39 |
| 54.42 | 454.72 |
| 56.85 | 448.71 |
| 42.48 | 469.76 |
| 44.89 | 450.71 |
| 58.79 | 444.01 |
| 58.2 | 453.2 |
| 57.85 | 450.87 |
| 63.86 | 441.73 |
| 39.52 | 465.09 |
| 64.63 | 447.28 |
| 39.33 | 491.16 |
| 62.52 | 450.98 |
| 63.56 | 446.3 |
| 79.74 | 436.48 |
| 42.03 | 460.84 |
| 69.51 | 442.56 |
| 41.49 | 467.3 |
| 40.64 | 479.13 |
| 52.3 | 441.15 |
| 59.04 | 445.52 |
| 40.69 | 475.4 |
| 40.69 | 469.3 |
| 41.62 | 463.57 |
| 68.67 | 445.32 |
| 44.21 | 461.03 |
| 43.13 | 466.74 |
| 59.87 | 444.04 |
| 67.25 | 434.01 |
| 44.9 | 465.23 |
| 69.34 | 440.6 |
| 44.84 | 466.74 |
| 75.6 | 433.48 |
| 40.96 | 473.59 |
| 41.74 | 474.81 |
| 44.06 | 454.75 |
| 49.69 | 452.94 |
| 69.13 | 435.83 |
| 39.22 | 482.19 |
| 44.47 | 466.66 |
| 40.12 | 462.59 |
| 64.63 | 447.82 |
| 41.62 | 462.73 |
| 63.21 | 447.98 |
| 39.31 | 462.72 |
| 58.46 | 442.42 |
| 48.41 | 444.69 |
| 39.0 | 466.7 |
| 64.63 | 453.84 |
| 67.32 | 436.92 |
| 36.08 | 486.37 |
| 60.07 | 440.43 |
| 63.07 | 446.82 |
| 39.48 | 484.91 |
| 71.64 | 437.76 |
| 68.08 | 438.91 |
| 39.54 | 464.19 |
| 67.79 | 442.19 |
| 47.43 | 446.86 |
| 44.2 | 457.15 |
| 43.65 | 482.57 |
| 41.26 | 476.03 |
| 72.58 | 428.89 |
| 40.23 | 472.7 |
| 52.3 | 445.6 |
| 52.05 | 464.78 |
| 58.95 | 440.42 |
| 70.79 | 428.41 |
| 70.47 | 438.5 |
| 59.43 | 438.28 |
| 40.64 | 476.29 |
| 58.49 | 448.46 |
| 57.19 | 438.99 |
| 39.22 | 471.8 |
| 42.44 | 471.81 |
| 59.14 | 449.82 |
| 68.08 | 442.14 |
| 58.79 | 441.46 |
| 40.22 | 477.62 |
| 61.87 | 446.76 |
| 39.82 | 472.52 |
| 40.71 | 471.58 |
| 72.39 | 440.85 |
| 77.54 | 431.37 |
| 71.97 | 437.33 |
| 40.71 | 469.22 |
| 40.78 | 471.11 |
| 66.51 | 439.17 |
| 62.26 | 445.33 |
| 39.22 | 473.71 |
| 57.76 | 452.66 |
| 59.43 | 440.99 |
| 38.91 | 467.42 |
| 58.82 | 444.14 |
| 56.53 | 457.17 |
| 54.3 | 467.87 |
| 70.72 | 442.04 |
| 44.58 | 471.36 |
| 39.1 | 460.7 |
| 70.83 | 431.33 |
| 76.86 | 432.6 |
| 59.39 | 447.61 |
| 64.79 | 443.87 |
| 52.3 | 446.87 |
| 41.01 | 465.74 |
| 56.89 | 447.86 |
| 62.96 | 447.65 |
| 63.47 | 437.87 |
| 41.66 | 483.51 |
| 41.46 | 479.65 |
| 58.16 | 455.16 |
| 71.25 | 431.91 |
| 41.5 | 470.68 |
| 69.13 | 429.28 |
| 59.21 | 450.81 |
| 73.5 | 437.73 |
| 44.6 | 460.21 |
| 69.05 | 442.86 |
| 45.0 | 482.99 |
| 65.27 | 440.0 |
| 41.82 | 478.48 |
| 49.82 | 455.28 |
| 66.56 | 436.94 |
| 37.87 | 461.06 |
| 69.45 | 438.28 |
| 39.58 | 472.61 |
| 70.79 | 426.85 |
| 39.72 | 470.18 |
| 54.42 | 455.38 |
| 66.56 | 428.32 |
| 42.49 | 480.35 |
| 56.53 | 455.56 |
| 58.33 | 447.66 |
| 68.67 | 443.06 |
| 58.86 | 452.43 |
| 41.54 | 477.81 |
| 73.77 | 431.66 |
| 73.91 | 431.8 |
| 68.67 | 446.67 |
| 60.29 | 445.26 |
| 69.89 | 425.72 |
| 72.29 | 430.58 |
| 60.27 | 439.86 |
| 59.15 | 441.11 |
| 71.14 | 434.72 |
| 67.45 | 434.01 |
| 41.17 | 475.64 |
| 41.58 | 460.44 |
| 68.67 | 436.4 |
| 40.73 | 461.03 |
| 41.54 | 479.08 |
| 69.14 | 435.76 |
| 45.51 | 460.14 |
| 75.6 | 442.2 |
| 50.78 | 447.69 |
| 73.67 | 431.15 |
| 63.56 | 445.0 |
| 73.21 | 431.59 |
| 44.47 | 467.22 |
| 51.43 | 445.33 |
| 45.09 | 470.57 |
| 39.61 | 473.77 |
| 60.29 | 447.67 |
| 40.05 | 474.29 |
| 71.32 | 437.14 |
| 69.05 | 432.56 |
| 41.79 | 459.14 |
| 60.07 | 446.19 |
| 71.77 | 428.1 |
| 44.47 | 468.46 |
| 66.75 | 435.02 |
| 48.6 | 445.52 |
| 40.55 | 462.69 |
| 61.27 | 455.75 |
| 40.0 | 463.74 |
| 69.68 | 439.79 |
| 58.95 | 443.26 |
| 69.13 | 432.04 |
| 43.96 | 465.86 |
| 45.87 | 465.6 |
| 25.36 | 469.43 |
| 49.3 | 440.75 |
| 38.91 | 481.32 |
| 40.56 | 479.87 |
| 39.16 | 458.59 |
| 71.97 | 438.62 |
| 59.44 | 445.59 |
| 43.56 | 481.87 |
| 41.5 | 475.01 |
| 48.41 | 436.54 |
| 40.66 | 456.63 |
| 62.52 | 451.69 |
| 39.59 | 463.04 |
| 67.71 | 446.1 |
| 59.92 | 438.67 |
| 41.4 | 466.88 |
| 48.41 | 444.6 |
| 59.39 | 440.26 |
| 40.96 | 483.92 |
| 41.2 | 475.19 |
| 44.68 | 479.24 |
| 73.68 | 434.92 |
| 65.46 | 454.16 |
| 58.79 | 447.58 |
| 41.26 | 467.9 |
| 69.13 | 426.29 |
| 45.87 | 447.02 |
| 41.79 | 455.85 |
| 40.22 | 476.46 |
| 68.94 | 437.48 |
| 49.21 | 452.77 |
| 39.33 | 491.54 |
| 64.79 | 438.41 |
| 41.93 | 476.1 |
| 36.99 | 464.58 |
| 40.78 | 467.74 |
| 57.17 | 442.12 |
| 57.5 | 453.34 |
| 69.13 | 425.29 |
| 51.43 | 449.63 |
| 45.78 | 462.88 |
| 42.32 | 464.67 |
| 35.57 | 489.96 |
| 38.08 | 482.38 |
| 77.95 | 437.95 |
| 71.98 | 429.2 |
| 46.63 | 453.34 |
| 70.02 | 442.47 |
| 49.69 | 462.6 |
| 40.96 | 478.79 |
| 52.05 | 456.11 |
| 50.16 | 450.33 |
| 69.88 | 434.83 |
| 73.68 | 433.43 |
| 62.96 | 456.02 |
| 40.67 | 485.23 |
| 37.14 | 473.57 |
| 39.58 | 469.94 |
| 49.83 | 452.07 |
| 41.04 | 475.32 |
| 36.24 | 480.69 |
| 48.06 | 444.01 |
| 56.03 | 465.17 |
| 39.82 | 480.61 |
| 41.5 | 476.04 |
| 49.3 | 441.76 |
| 71.98 | 428.24 |
| 67.71 | 444.77 |
| 45.87 | 463.1 |
| 44.6 | 470.5 |
| 72.99 | 431.0 |
| 69.4 | 430.68 |
| 67.17 | 436.42 |
| 49.82 | 452.33 |
| 63.94 | 440.16 |
| 63.76 | 435.75 |
| 44.57 | 449.74 |
| 72.24 | 430.73 |
| 77.17 | 432.75 |
| 47.43 | 446.79 |
| 41.39 | 486.35 |
| 59.8 | 453.18 |
| 50.23 | 458.31 |
| 41.54 | 480.26 |
| 51.3 | 448.65 |
| 49.5 | 458.41 |
| 64.69 | 435.39 |
| 50.16 | 450.21 |
| 43.14 | 459.59 |
| 75.6 | 445.84 |
| 48.78 | 441.08 |
| 37.85 | 467.33 |
| 63.09 | 444.19 |
| 68.27 | 432.96 |
| 47.93 | 438.09 |
| 36.99 | 467.9 |
| 43.67 | 475.72 |
| 34.69 | 477.51 |
| 69.84 | 435.13 |
| 39.61 | 477.9 |
| 44.2 | 457.26 |
| 44.6 | 467.53 |
| 41.58 | 465.15 |
| 40.1 | 474.28 |
| 65.71 | 444.49 |
| 45.09 | 452.84 |
| 77.54 | 435.38 |
| 75.33 | 433.57 |
| 69.71 | 435.27 |
| 39.63 | 468.49 |
| 70.8 | 433.07 |
| 73.56 | 430.63 |
| 65.74 | 440.74 |
| 39.96 | 474.49 |
| 48.6 | 449.74 |
| 63.76 | 436.73 |
| 70.79 | 434.58 |
| 43.13 | 473.93 |
| 79.74 | 435.99 |
| 43.22 | 466.83 |
| 68.24 | 427.22 |
| 63.77 | 444.07 |
| 41.49 | 469.57 |
| 66.51 | 459.89 |
| 40.71 | 479.59 |
| 64.69 | 440.92 |
| 41.46 | 480.87 |
| 66.54 | 441.9 |
| 69.68 | 430.2 |
| 42.86 | 465.16 |
| 44.45 | 471.32 |
| 40.64 | 485.43 |
| 40.07 | 495.35 |
| 58.49 | 449.12 |
| 42.02 | 480.53 |
| 50.66 | 457.07 |
| 69.94 | 443.67 |
| 44.68 | 477.52 |
| 39.64 | 472.95 |
| 39.69 | 472.54 |
| 40.71 | 469.17 |
| 70.04 | 435.21 |
| 40.22 | 477.78 |
| 39.85 | 475.89 |
| 40.23 | 483.9 |
| 39.16 | 476.2 |
| 43.34 | 462.16 |
| 37.85 | 471.05 |
| 41.26 | 484.71 |
| 63.86 | 446.34 |
| 42.28 | 469.02 |
| 72.86 | 432.12 |
| 40.1 | 467.28 |
| 69.98 | 429.66 |
| 45.09 | 469.49 |
| 40.56 | 485.87 |
| 40.81 | 481.95 |
| 40.02 | 479.03 |
| 69.13 | 434.5 |
| 54.3 | 464.9 |
| 63.94 | 452.71 |
| 69.51 | 429.74 |
| 56.65 | 457.09 |
| 72.29 | 446.77 |
| 41.48 | 460.76 |
| 40.83 | 471.95 |
| 46.21 | 453.29 |
| 60.07 | 441.61 |
| 46.18 | 464.73 |
| 43.69 | 464.68 |
| 73.91 | 430.59 |
| 77.95 | 438.01 |
| 41.04 | 479.08 |
| 74.78 | 436.39 |
| 65.46 | 447.07 |
| 39.58 | 479.91 |
| 43.02 | 489.05 |
| 53.82 | 463.17 |
| 42.02 | 471.26 |
| 40.67 | 480.49 |
| 39.42 | 473.78 |
| 58.16 | 455.5 |
| 58.41 | 446.27 |
| 41.06 | 482.2 |
| 59.8 | 452.48 |
| 43.14 | 464.48 |
| 69.89 | 438.1 |
| 63.47 | 445.6 |
| 67.79 | 442.43 |
| 61.25 | 436.67 |
| 41.85 | 466.56 |
| 71.14 | 457.29 |
| 36.25 | 487.03 |
| 52.72 | 464.93 |
| 44.63 | 466.0 |
| 48.79 | 469.52 |
| 70.04 | 428.88 |
| 41.48 | 474.3 |
| 39.31 | 461.06 |
| 39.39 | 465.57 |
| 41.78 | 467.67 |
| 40.71 | 466.99 |
| 39.39 | 463.72 |
| 49.16 | 443.78 |
| 68.28 | 445.23 |
| 40.66 | 464.43 |
| 36.25 | 484.36 |
| 63.07 | 442.16 |
| 39.63 | 464.11 |
| 49.39 | 462.48 |
| 42.49 | 477.49 |
| 65.59 | 437.04 |
| 40.79 | 457.09 |
| 45.01 | 450.6 |
| 45.0 | 465.78 |
| 70.04 | 427.1 |
| 44.63 | 459.81 |
| 47.43 | 447.36 |
| 39.64 | 488.92 |
| 66.49 | 433.36 |
| 39.04 | 483.35 |
| 41.04 | 469.53 |
| 39.82 | 476.96 |
| 58.46 | 440.75 |
| 43.71 | 462.55 |
| 54.2 | 448.04 |
| 50.59 | 455.24 |
| 40.07 | 494.75 |
| 57.32 | 444.58 |
| 35.79 | 484.82 |
| 66.05 | 442.9 |
| 39.33 | 485.46 |
| 49.5 | 457.81 |
| 43.65 | 481.92 |
| 61.02 | 443.23 |
| 39.69 | 474.29 |
| 71.58 | 430.46 |
| 50.66 | 455.71 |
| 62.26 | 438.34 |
| 41.31 | 485.83 |
| 44.06 | 452.82 |
| 68.94 | 435.04 |
| 59.8 | 451.21 |
| 42.74 | 465.81 |
| 44.92 | 458.42 |
| 39.96 | 470.22 |
| 49.39 | 449.24 |
| 44.92 | 471.43 |
| 44.92 | 473.26 |
| 58.49 | 452.82 |
| 70.32 | 432.69 |
| 60.84 | 444.13 |
| 41.92 | 467.21 |
| 48.6 | 445.98 |
| 73.77 | 436.91 |
| 44.9 | 455.01 |
| 66.56 | 437.11 |
| 40.64 | 477.06 |
| 67.71 | 441.71 |
| 40.07 | 495.76 |
| 66.44 | 445.63 |
| 41.62 | 464.72 |
| 68.94 | 438.03 |
| 72.86 | 434.78 |
| 60.32 | 444.67 |
| 61.41 | 452.24 |
| 45.09 | 450.92 |
| 70.4 | 436.53 |
| 71.77 | 435.53 |
| 68.51 | 440.01 |
| 64.45 | 443.1 |
| 71.94 | 427.49 |
| 68.08 | 436.25 |
| 67.79 | 440.74 |
| 63.31 | 443.54 |
| 51.43 | 459.42 |
| 60.23 | 439.66 |
| 44.84 | 464.15 |
| 56.65 | 459.1 |
| 52.36 | 455.68 |
| 45.51 | 469.08 |
| 41.26 | 478.02 |
| 44.06 | 456.8 |
| 44.89 | 441.13 |
| 43.69 | 463.88 |
| 73.67 | 430.45 |
| 65.71 | 449.18 |
| 48.92 | 447.89 |
| 70.04 | 431.59 |
| 52.3 | 447.5 |
| 41.82 | 475.58 |
| 59.8 | 453.24 |
| 63.21 | 446.4 |
| 44.92 | 476.81 |
| 40.46 | 474.1 |
| 57.76 | 450.71 |
| 66.48 | 433.62 |
| 41.66 | 465.14 |
| 59.87 | 445.18 |
| 39.22 | 474.12 |
| 43.79 | 483.91 |
| 40.27 | 486.68 |
| 43.52 | 464.98 |
| 45.87 | 481.4 |
| 39.04 | 479.2 |
| 41.16 | 463.86 |
| 38.25 | 472.3 |
| 58.84 | 446.51 |
| 51.43 | 437.71 |
| 41.1 | 458.94 |
| 67.17 | 437.91 |
| 39.33 | 490.76 |
| 65.06 | 439.66 |
| 44.9 | 463.27 |
| 39.52 | 473.99 |
| 66.49 | 433.38 |
| 53.82 | 459.01 |
| 40.75 | 471.44 |
| 39.28 | 471.91 |
| 44.47 | 465.15 |
| 51.19 | 446.66 |
| 63.76 | 438.15 |
| 51.19 | 447.14 |
| 43.71 | 472.32 |
| 70.72 | 441.68 |
| 72.99 | 440.04 |
| 64.05 | 444.82 |
| 53.16 | 457.26 |
| 72.58 | 428.83 |
| 43.43 | 449.07 |
| 71.94 | 435.21 |
| 44.2 | 471.03 |
| 48.04 | 465.56 |
| 62.96 | 442.83 |
| 41.48 | 460.3 |
| 40.71 | 474.25 |
| 41.82 | 477.97 |
| 40.46 | 472.16 |
| 46.97 | 456.08 |
| 49.82 | 452.41 |
| 43.22 | 463.71 |
| 67.07 | 433.72 |
| 57.76 | 456.4 |
| 48.98 | 448.43 |
| 36.08 | 481.6 |
| 42.18 | 457.07 |
| 49.39 | 451.0 |
| 75.6 | 440.28 |
| 71.32 | 437.47 |
| 67.71 | 443.57 |
| 69.84 | 426.6 |
| 41.5 | 470.87 |
| 39.85 | 478.37 |
| 58.66 | 453.92 |
| 40.56 | 470.22 |
| 72.24 | 434.54 |
| 63.9 | 442.89 |
| 36.24 | 479.03 |
| 43.99 | 476.06 |
| 36.71 | 473.88 |
| 57.25 | 451.75 |
| 56.85 | 439.2 |
| 58.46 | 439.7 |
| 46.18 | 463.6 |
| 52.84 | 447.47 |
| 56.89 | 447.92 |
| 44.63 | 471.08 |
| 72.43 | 437.55 |
| 51.3 | 448.27 |
| 72.39 | 431.69 |
| 48.14 | 449.09 |
| 58.46 | 448.79 |
| 44.92 | 460.21 |
| 41.17 | 479.28 |
| 41.82 | 483.11 |
| 54.2 | 450.75 |
| 66.54 | 437.97 |
| 41.48 | 459.76 |
| 44.9 | 457.75 |
| 40.1 | 469.33 |
| 69.75 | 433.28 |
| 70.02 | 444.64 |
| 44.9 | 463.1 |
| 44.68 | 460.91 |
| 39.04 | 479.35 |
| 59.21 | 449.23 |
| 40.92 | 474.51 |
| 72.99 | 435.02 |
| 70.72 | 435.45 |
| 48.14 | 452.38 |
| 39.3 | 480.41 |
| 39.72 | 478.96 |
| 42.74 | 468.87 |
| 76.2 | 434.01 |
| 44.6 | 466.36 |
| 75.23 | 435.28 |
| 43.02 | 486.46 |
| 40.1 | 468.19 |
| 41.01 | 468.37 |
| 41.39 | 474.19 |
| 74.22 | 440.32 |
| 38.44 | 485.32 |
| 42.86 | 464.27 |
| 36.18 | 479.25 |
| 70.32 | 430.4 |
| 58.62 | 447.49 |
| 64.69 | 438.23 |
| 40.43 | 492.09 |
| 40.75 | 475.36 |
| 54.9 | 452.56 |
| 74.67 | 427.84 |
| 68.08 | 433.95 |
| 70.8 | 435.27 |
| 58.59 | 454.62 |
| 40.56 | 472.17 |
| 63.31 | 452.42 |
| 42.02 | 472.17 |
| 41.44 | 481.83 |
| 49.15 | 458.78 |
| 62.08 | 447.5 |
| 49.25 | 463.4 |
| 41.26 | 473.57 |
| 70.32 | 433.72 |
| 66.44 | 431.85 |
| 77.95 | 433.47 |
| 74.22 | 432.84 |
| 70.94 | 436.6 |
| 41.17 | 490.23 |
| 41.74 | 477.16 |
| 68.31 | 441.06 |
| 70.98 | 440.86 |
| 41.82 | 477.94 |
| 40.8 | 474.47 |
| 43.02 | 470.67 |
| 65.34 | 447.31 |
| 44.88 | 466.8 |
| 71.94 | 430.91 |
| 69.88 | 434.75 |
| 42.86 | 469.52 |
| 59.43 | 438.9 |
| 70.04 | 429.56 |
| 74.33 | 432.92 |
| 49.16 | 442.87 |
| 41.76 | 466.59 |
| 41.14 | 479.61 |
| 44.63 | 471.08 |
| 59.14 | 433.37 |
| 65.48 | 443.92 |
| 59.14 | 443.5 |
| 64.79 | 439.89 |
| 69.59 | 434.66 |
| 41.38 | 487.57 |
| 41.62 | 464.64 |
| 42.32 | 470.92 |
| 63.73 | 444.39 |
| 48.6 | 442.48 |
| 52.08 | 449.61 |
| 69.34 | 435.02 |
| 43.99 | 458.67 |
| 46.18 | 461.74 |
| 62.39 | 438.31 |
| 48.92 | 462.38 |
| 44.2 | 460.56 |
| 59.21 | 439.22 |
| 58.79 | 444.64 |
| 71.25 | 430.34 |
| 71.29 | 430.46 |
| 45.87 | 456.79 |
| 41.23 | 468.82 |
| 44.9 | 448.51 |
| 44.71 | 470.77 |
| 41.14 | 465.74 |
| 66.56 | 430.21 |
| 50.32 | 449.23 |
| 49.15 | 461.89 |
| 61.45 | 445.72 |
| 39.39 | 466.13 |
| 51.19 | 448.71 |
| 41.23 | 469.25 |
| 66.86 | 450.56 |
| 39.64 | 464.46 |
| 41.5 | 471.13 |
| 48.06 | 461.52 |
| 59.15 | 451.09 |
| 79.74 | 431.51 |
| 40.83 | 469.8 |
| 51.43 | 442.28 |
| 48.92 | 458.67 |
| 43.79 | 462.4 |
| 59.8 | 453.54 |
| 62.96 | 444.38 |
| 58.9 | 440.52 |
| 69.14 | 433.62 |
| 45.0 | 481.96 |
| 44.9 | 452.75 |
| 39.42 | 481.28 |
| 66.05 | 439.03 |
| 73.88 | 435.75 |
| 74.16 | 436.03 |
| 58.49 | 445.6 |
| 56.03 | 462.65 |
| 50.05 | 438.66 |
| 50.32 | 447.32 |
| 39.48 | 484.55 |
| 41.01 | 476.8 |
| 39.04 | 480.34 |
| 48.41 | 440.63 |
| 44.6 | 459.48 |
| 40.43 | 490.78 |
| 38.91 | 483.56 |
| 73.18 | 429.38 |
| 59.21 | 440.27 |
| 51.43 | 445.34 |
| 65.27 | 447.43 |
| 69.94 | 439.91 |
| 50.59 | 459.27 |
| 39.61 | 478.89 |
| 41.66 | 466.7 |
| 43.5 | 463.5 |
| 74.33 | 436.21 |
| 63.47 | 443.94 |
| 63.94 | 439.63 |
| 41.54 | 460.95 |
| 59.14 | 448.69 |
| 75.08 | 444.63 |
| 37.83 | 473.51 |
| 39.54 | 462.56 |
| 50.16 | 451.76 |
| 40.43 | 491.81 |
| 68.08 | 429.52 |
| 49.02 | 437.9 |
| 38.73 | 467.54 |
| 64.45 | 449.97 |
| 66.48 | 436.62 |
| 40.55 | 477.68 |
| 63.86 | 447.26 |
| 63.13 | 439.76 |
| 59.39 | 437.49 |
| 58.2 | 455.14 |
| 41.03 | 485.5 |
| 66.93 | 444.1 |
| 70.94 | 432.33 |
| 44.03 | 471.23 |
| 42.34 | 463.89 |
| 51.19 | 445.54 |
| 59.54 | 446.09 |
| 63.86 | 445.12 |
| 59.21 | 443.31 |
| 39.66 | 484.16 |
| 40.69 | 477.76 |
| 71.29 | 430.28 |
| 62.04 | 446.48 |
| 36.66 | 481.03 |
| 47.83 | 466.07 |
| 67.32 | 447.47 |
| 55.5 | 455.93 |
| 40.0 | 479.62 |
| 47.01 | 455.06 |
| 40.46 | 475.06 |
| 56.85 | 438.89 |
| 69.84 | 432.7 |
| 50.9 | 452.6 |
| 46.21 | 451.75 |
| 71.06 | 430.66 |
| 38.91 | 491.9 |
| 74.78 | 439.82 |
| 39.0 | 460.73 |
| 60.95 | 449.7 |
| 59.15 | 439.42 |
| 65.06 | 439.84 |
| 35.57 | 485.86 |
| 40.12 | 458.1 |
| 39.82 | 479.92 |
| 56.03 | 458.29 |
| 40.07 | 489.45 |
| 73.42 | 434.0 |
| 69.13 | 431.24 |
| 47.93 | 439.5 |
| 40.56 | 467.46 |
| 71.58 | 429.27 |
| 58.95 | 452.1 |
| 42.02 | 472.41 |
| 69.05 | 442.14 |
| 49.3 | 441.0 |
| 41.58 | 463.07 |
| 60.84 | 445.71 |
| 39.81 | 483.16 |
| 68.84 | 440.45 |
| 38.06 | 481.83 |
| 40.62 | 467.6 |
| 52.84 | 450.88 |
| 69.13 | 425.5 |
| 54.42 | 451.87 |
| 69.51 | 428.94 |
| 48.6 | 439.86 |
| 73.42 | 433.44 |
| 73.68 | 438.23 |
| 71.32 | 436.95 |
| 40.56 | 470.19 |
| 40.22 | 484.66 |
| 68.27 | 430.81 |
| 73.17 | 433.37 |
| 48.98 | 453.02 |
| 50.9 | 453.5 |
| 41.23 | 463.09 |
| 44.6 | 464.56 |
| 65.46 | 452.12 |
| 40.69 | 470.9 |
| 64.15 | 450.89 |
| 63.76 | 445.04 |
| 63.07 | 444.72 |
| 40.92 | 460.38 |
| 58.33 | 446.8 |
| 44.06 | 465.05 |
| 40.62 | 484.13 |
| 39.81 | 488.27 |
| 49.21 | 447.09 |
| 58.16 | 452.02 |
| 58.96 | 455.55 |
| 41.92 | 480.99 |
| 41.67 | 467.68 |
| Pressure | Power |
|---|---|
| 1024.07 | 463.26 |
| 1020.04 | 444.37 |
| 1012.16 | 488.56 |
| 1010.24 | 446.48 |
| 1009.23 | 473.9 |
| 1012.23 | 443.67 |
| 1014.02 | 467.35 |
| 1019.12 | 478.42 |
| 1021.78 | 475.98 |
| 1015.14 | 477.5 |
| 1008.64 | 453.02 |
| 1014.66 | 453.99 |
| 1011.31 | 440.29 |
| 1012.77 | 451.28 |
| 1009.48 | 433.99 |
| 1015.76 | 462.19 |
| 1022.86 | 467.54 |
| 1022.6 | 477.2 |
| 1023.97 | 459.85 |
| 1015.15 | 464.3 |
| 1009.09 | 468.27 |
| 1019.16 | 495.24 |
| 1008.52 | 483.8 |
| 1014.3 | 443.61 |
| 1003.18 | 436.06 |
| 1009.97 | 443.25 |
| 1011.11 | 464.16 |
| 1023.57 | 475.52 |
| 1012.14 | 484.41 |
| 1012.69 | 437.89 |
| 1019.02 | 445.11 |
| 1013.64 | 438.86 |
| 1011.11 | 440.98 |
| 1010.08 | 436.65 |
| 1018.76 | 444.26 |
| 1007.29 | 465.86 |
| 1011.4 | 444.37 |
| 1010.08 | 450.69 |
| 1014.69 | 469.02 |
| 1012.04 | 448.86 |
| 1007.62 | 447.14 |
| 1017.24 | 469.18 |
| 1018.33 | 482.8 |
| 1014.12 | 476.7 |
| 1020.63 | 474.99 |
| 1019.28 | 444.22 |
| 1020.24 | 461.33 |
| 1010.96 | 448.06 |
| 1014.51 | 474.6 |
| 1029.14 | 473.05 |
| 1010.58 | 432.06 |
| 1018.71 | 467.41 |
| 1011.6 | 430.12 |
| 1014.82 | 473.62 |
| 1012.94 | 471.81 |
| 1010.23 | 442.99 |
| 1014.65 | 442.77 |
| 1010.18 | 491.49 |
| 1013.68 | 447.46 |
| 1002.25 | 446.11 |
| 1007.03 | 442.44 |
| 1013.61 | 446.22 |
| 1016.67 | 471.49 |
| 1008.89 | 463.5 |
| 1016.02 | 440.01 |
| 1014.56 | 441.03 |
| 1019.49 | 452.68 |
| 1020.43 | 474.91 |
| 1018.71 | 478.77 |
| 998.14 | 434.2 |
| 1017.83 | 437.91 |
| 1024.61 | 477.61 |
| 1016.65 | 431.65 |
| 998.58 | 430.57 |
| 1008.82 | 481.09 |
| 1009.56 | 445.56 |
| 1012.71 | 475.74 |
| 1010.36 | 435.12 |
| 1017.39 | 446.15 |
| 1001.31 | 436.64 |
| 1013.89 | 436.69 |
| 1016.53 | 468.75 |
| 1012.18 | 466.6 |
| 1024.42 | 465.48 |
| 1014.93 | 441.34 |
| 1012.53 | 441.83 |
| 1019.0 | 464.7 |
| 1009.6 | 437.99 |
| 1015.55 | 459.12 |
| 1006.56 | 429.69 |
| 1010.57 | 459.8 |
| 1009.81 | 433.63 |
| 1014.62 | 442.84 |
| 1014.49 | 485.13 |
| 1025.09 | 459.12 |
| 1012.05 | 445.31 |
| 1022.7 | 480.8 |
| 1005.23 | 432.55 |
| 1013.42 | 443.86 |
| 1018.3 | 449.77 |
| 1017.19 | 470.71 |
| 1011.93 | 452.17 |
| 1021.6 | 478.29 |
| 1006.96 | 428.54 |
| 1020.66 | 478.27 |
| 1007.63 | 439.58 |
| 1005.53 | 457.32 |
| 1018.79 | 475.51 |
| 1010.85 | 439.66 |
| 1011.03 | 471.99 |
| 1015.1 | 479.81 |
| 1010.83 | 434.78 |
| 1011.18 | 446.58 |
| 1013.92 | 437.76 |
| 1001.24 | 459.36 |
| 1016.99 | 462.28 |
| 1016.08 | 464.33 |
| 1010.71 | 444.36 |
| 1014.32 | 438.64 |
| 1018.69 | 470.49 |
| 1001.86 | 455.13 |
| 1017.06 | 450.22 |
| 1009.34 | 440.43 |
| 1014.02 | 482.98 |
| 1014.19 | 460.44 |
| 1012.81 | 444.97 |
| 1005.2 | 433.94 |
| 1012.28 | 439.73 |
| 1005.89 | 434.48 |
| 1017.93 | 442.33 |
| 1014.26 | 457.67 |
| 1018.8 | 454.66 |
| 1017.71 | 432.21 |
| 1012.25 | 457.66 |
| 1010.27 | 435.21 |
| 1007.64 | 448.22 |
| 1013.79 | 475.51 |
| 1013.24 | 446.53 |
| 1011.7 | 441.3 |
| 1007.67 | 433.54 |
| 1020.45 | 472.52 |
| 1012.59 | 474.77 |
| 1001.62 | 435.1 |
| 1016.92 | 450.74 |
| 1014.32 | 442.7 |
| 1003.34 | 426.56 |
| 1016.0 | 463.71 |
| 1016.85 | 447.06 |
| 1018.02 | 452.27 |
| 1018.49 | 445.78 |
| 1017.93 | 438.65 |
| 1011.55 | 480.15 |
| 1016.46 | 447.19 |
| 1008.47 | 443.04 |
| 1008.06 | 488.81 |
| 1016.26 | 455.75 |
| 1018.83 | 455.86 |
| 1013.94 | 457.68 |
| 1014.95 | 479.11 |
| 1007.44 | 432.84 |
| 1016.18 | 448.37 |
| 1007.56 | 447.06 |
| 1014.1 | 443.53 |
| 1011.13 | 445.21 |
| 1015.53 | 441.7 |
| 1013.05 | 450.93 |
| 1002.95 | 451.44 |
| 1012.32 | 441.29 |
| 1014.48 | 458.85 |
| 1018.24 | 481.46 |
| 1016.93 | 467.19 |
| 1020.5 | 461.54 |
| 1006.28 | 439.08 |
| 1028.79 | 467.22 |
| 1019.49 | 468.8 |
| 1004.66 | 426.93 |
| 1024.36 | 474.65 |
| 1022.93 | 468.97 |
| 1010.18 | 433.97 |
| 1009.52 | 450.53 |
| 1011.98 | 444.51 |
| 1015.29 | 469.03 |
| 1019.36 | 466.56 |
| 1012.15 | 457.57 |
| 1006.24 | 440.13 |
| 1003.57 | 433.24 |
| 1008.64 | 452.55 |
| 1002.04 | 443.29 |
| 1005.47 | 431.76 |
| 1007.81 | 454.97 |
| 1012.42 | 456.7 |
| 1007.75 | 486.03 |
| 1011.37 | 472.79 |
| 1010.12 | 452.03 |
| 1012.26 | 443.41 |
| 1014.49 | 441.93 |
| 1007.51 | 432.64 |
| 1013.15 | 480.25 |
| 1004.47 | 466.68 |
| 1020.19 | 494.39 |
| 1012.46 | 454.72 |
| 1012.28 | 448.71 |
| 1013.43 | 469.76 |
| 1009.64 | 450.71 |
| 1009.8 | 444.01 |
| 1017.46 | 453.2 |
| 1012.39 | 450.87 |
| 1019.67 | 441.73 |
| 1018.41 | 465.09 |
| 1020.59 | 447.28 |
| 1011.54 | 491.16 |
| 1017.99 | 450.98 |
| 1013.75 | 446.3 |
| 1008.37 | 436.48 |
| 1017.41 | 460.84 |
| 1013.43 | 442.56 |
| 1020.19 | 467.3 |
| 1021.47 | 479.13 |
| 1008.72 | 441.15 |
| 1011.78 | 445.52 |
| 1015.62 | 475.4 |
| 1014.91 | 469.3 |
| 1017.17 | 463.57 |
| 1006.71 | 445.32 |
| 1020.36 | 461.03 |
| 1014.99 | 466.74 |
| 1018.47 | 444.04 |
| 1017.6 | 434.01 |
| 1020.5 | 465.23 |
| 1009.63 | 440.6 |
| 1023.66 | 466.74 |
| 1017.43 | 433.48 |
| 1023.36 | 473.59 |
| 1020.75 | 474.81 |
| 1017.58 | 454.75 |
| 1009.6 | 452.94 |
| 1009.94 | 435.83 |
| 1014.53 | 482.19 |
| 1030.46 | 466.66 |
| 1013.03 | 462.59 |
| 1020.69 | 447.82 |
| 1014.55 | 462.73 |
| 1012.06 | 447.98 |
| 1009.15 | 462.72 |
| 1016.82 | 442.42 |
| 1008.64 | 444.69 |
| 1016.73 | 466.7 |
| 1020.38 | 453.84 |
| 1013.83 | 436.92 |
| 1021.82 | 486.37 |
| 1015.42 | 440.43 |
| 1010.94 | 446.82 |
| 1005.11 | 484.91 |
| 1008.27 | 437.76 |
| 1013.27 | 438.91 |
| 1007.97 | 464.19 |
| 1009.89 | 442.19 |
| 1008.38 | 446.86 |
| 1018.89 | 457.15 |
| 1020.14 | 482.57 |
| 1007.44 | 476.03 |
| 1008.69 | 428.89 |
| 1018.07 | 472.7 |
| 1009.04 | 445.6 |
| 1014.63 | 464.78 |
| 1017.02 | 440.42 |
| 1003.96 | 428.41 |
| 1010.65 | 438.5 |
| 1007.84 | 438.28 |
| 1022.35 | 476.29 |
| 1011.5 | 448.46 |
| 1008.62 | 438.99 |
| 1017.9 | 471.8 |
| 1012.74 | 471.81 |
| 1016.12 | 449.82 |
| 1013.13 | 442.14 |
| 1009.74 | 441.46 |
| 1011.37 | 477.62 |
| 1011.45 | 446.76 |
| 1012.46 | 472.52 |
| 1021.27 | 471.58 |
| 1001.15 | 440.85 |
| 1011.33 | 431.37 |
| 1008.64 | 437.33 |
| 1015.68 | 469.22 |
| 1023.51 | 471.11 |
| 1010.98 | 439.17 |
| 1012.11 | 445.33 |
| 1018.62 | 473.71 |
| 1016.28 | 452.66 |
| 1007.12 | 440.99 |
| 1014.48 | 467.42 |
| 1010.02 | 444.14 |
| 1020.57 | 457.17 |
| 1015.92 | 467.87 |
| 1009.78 | 442.04 |
| 1019.52 | 471.36 |
| 1009.81 | 460.7 |
| 1010.35 | 431.33 |
| 998.59 | 432.6 |
| 1014.07 | 447.61 |
| 1016.27 | 443.87 |
| 1009.2 | 446.87 |
| 1020.57 | 465.74 |
| 1014.02 | 447.86 |
| 1020.16 | 447.65 |
| 1011.78 | 437.87 |
| 1016.87 | 483.51 |
| 1018.21 | 479.65 |
| 1016.88 | 455.16 |
| 1002.49 | 431.91 |
| 1014.13 | 470.68 |
| 1009.29 | 429.28 |
| 1018.32 | 450.81 |
| 1011.49 | 437.73 |
| 1015.16 | 460.21 |
| 1001.6 | 442.86 |
| 1021.51 | 482.99 |
| 1013.27 | 440.0 |
| 1033.25 | 478.48 |
| 1015.01 | 455.28 |
| 1002.07 | 436.94 |
| 1022.36 | 461.06 |
| 1013.97 | 438.28 |
| 1011.81 | 472.61 |
| 1003.7 | 426.85 |
| 1017.76 | 470.18 |
| 1012.31 | 455.38 |
| 1006.44 | 428.32 |
| 1010.57 | 480.35 |
| 1020.2 | 455.56 |
| 1013.14 | 447.66 |
| 1006.74 | 443.06 |
| 1014.19 | 452.43 |
| 1019.94 | 477.81 |
| 1004.72 | 431.66 |
| 1004.53 | 431.8 |
| 1006.65 | 446.67 |
| 1018.0 | 445.26 |
| 1013.85 | 425.72 |
| 1008.73 | 430.58 |
| 1018.94 | 439.86 |
| 1013.31 | 441.11 |
| 1011.6 | 434.72 |
| 1014.23 | 434.01 |
| 1019.43 | 475.64 |
| 1020.43 | 460.44 |
| 1005.46 | 436.4 |
| 1018.7 | 461.03 |
| 1019.94 | 479.08 |
| 1009.31 | 435.76 |
| 1015.22 | 460.14 |
| 1017.37 | 442.2 |
| 1008.83 | 447.69 |
| 1006.65 | 431.15 |
| 1014.32 | 445.0 |
| 1001.32 | 431.59 |
| 1027.94 | 467.22 |
| 1012.16 | 445.33 |
| 1013.21 | 470.57 |
| 1018.72 | 473.77 |
| 1017.42 | 447.67 |
| 1014.78 | 474.29 |
| 1008.07 | 437.14 |
| 1003.12 | 432.56 |
| 1009.72 | 459.14 |
| 1016.03 | 446.19 |
| 1006.38 | 428.1 |
| 1030.18 | 468.46 |
| 1017.95 | 435.02 |
| 1002.38 | 445.52 |
| 1003.36 | 462.69 |
| 1019.26 | 455.75 |
| 1015.89 | 463.74 |
| 1011.95 | 439.79 |
| 1016.99 | 443.26 |
| 1009.3 | 432.04 |
| 1013.32 | 465.86 |
| 1009.05 | 465.6 |
| 1009.35 | 469.43 |
| 1003.4 | 440.75 |
| 1015.82 | 481.32 |
| 1022.64 | 479.87 |
| 1005.7 | 458.59 |
| 1009.62 | 438.62 |
| 1012.38 | 445.59 |
| 1015.13 | 481.87 |
| 1013.58 | 475.01 |
| 1008.53 | 436.54 |
| 1016.28 | 456.63 |
| 1015.63 | 451.69 |
| 1010.93 | 463.04 |
| 1007.68 | 446.1 |
| 1009.94 | 438.67 |
| 1019.7 | 466.88 |
| 1008.42 | 444.6 |
| 1015.4 | 440.26 |
| 1023.28 | 483.92 |
| 1017.18 | 475.19 |
| 1023.06 | 479.24 |
| 1014.95 | 434.92 |
| 1014.0 | 454.16 |
| 1012.42 | 447.58 |
| 1021.67 | 467.9 |
| 1010.27 | 426.29 |
| 1007.8 | 447.02 |
| 1005.47 | 455.85 |
| 1010.31 | 476.46 |
| 1007.53 | 437.48 |
| 1014.61 | 452.77 |
| 1012.57 | 491.54 |
| 1017.22 | 438.41 |
| 1019.81 | 476.1 |
| 1007.87 | 464.58 |
| 1023.91 | 467.74 |
| 1010.0 | 442.12 |
| 1014.53 | 453.34 |
| 1010.44 | 425.29 |
| 1011.74 | 449.63 |
| 1025.27 | 462.88 |
| 1015.71 | 464.67 |
| 1027.17 | 489.96 |
| 1020.27 | 482.38 |
| 1009.45 | 437.95 |
| 1004.74 | 429.2 |
| 1013.03 | 453.34 |
| 1010.58 | 442.47 |
| 1015.14 | 462.6 |
| 1024.57 | 478.79 |
| 1012.34 | 456.11 |
| 1005.81 | 450.33 |
| 1007.21 | 434.83 |
| 1015.1 | 433.43 |
| 1020.76 | 456.02 |
| 1018.08 | 485.23 |
| 1013.03 | 473.57 |
| 1011.17 | 469.94 |
| 1008.69 | 452.07 |
| 1021.82 | 475.32 |
| 1013.39 | 480.69 |
| 1013.12 | 444.01 |
| 1020.41 | 465.17 |
| 1012.87 | 480.61 |
| 1013.39 | 476.04 |
| 1003.51 | 441.76 |
| 1005.19 | 428.24 |
| 1004.0 | 444.77 |
| 1008.58 | 463.1 |
| 1018.19 | 470.5 |
| 1007.04 | 431.0 |
| 1004.27 | 430.68 |
| 1006.6 | 436.42 |
| 1016.19 | 452.33 |
| 1010.64 | 440.16 |
| 1010.18 | 435.75 |
| 1008.48 | 449.74 |
| 1010.74 | 430.73 |
| 1009.55 | 432.75 |
| 1007.88 | 446.79 |
| 1018.12 | 486.35 |
| 1015.66 | 453.18 |
| 1015.73 | 458.31 |
| 1019.7 | 480.26 |
| 1011.89 | 448.65 |
| 1012.67 | 458.41 |
| 1006.37 | 435.39 |
| 1010.49 | 450.21 |
| 1018.68 | 459.59 |
| 1017.19 | 445.84 |
| 1018.17 | 441.08 |
| 1009.89 | 467.33 |
| 1016.56 | 444.19 |
| 1007.96 | 432.96 |
| 1002.85 | 438.09 |
| 1006.86 | 467.9 |
| 1012.68 | 475.72 |
| 1027.72 | 477.51 |
| 1006.37 | 435.13 |
| 1017.99 | 477.9 |
| 1019.18 | 457.26 |
| 1015.88 | 467.53 |
| 1021.08 | 465.15 |
| 1014.42 | 474.28 |
| 1013.85 | 444.49 |
| 1014.15 | 452.84 |
| 1008.5 | 435.38 |
| 1003.88 | 433.57 |
| 1009.04 | 435.27 |
| 1005.35 | 468.49 |
| 1009.9 | 433.07 |
| 1004.85 | 430.63 |
| 1013.29 | 440.74 |
| 1026.31 | 474.49 |
| 1005.72 | 449.74 |
| 1010.09 | 436.73 |
| 1006.53 | 434.58 |
| 1017.24 | 473.93 |
| 1007.03 | 435.99 |
| 1009.45 | 466.83 |
| 1005.29 | 427.22 |
| 1013.39 | 444.07 |
| 1020.11 | 469.57 |
| 1015.18 | 459.89 |
| 1024.91 | 479.59 |
| 1007.21 | 440.92 |
| 1020.45 | 480.87 |
| 1009.93 | 441.9 |
| 1011.35 | 430.2 |
| 1014.62 | 465.16 |
| 1021.19 | 471.32 |
| 1020.91 | 485.43 |
| 1012.27 | 495.35 |
| 1010.85 | 449.12 |
| 1006.22 | 480.53 |
| 1014.89 | 457.07 |
| 1010.7 | 443.67 |
| 1023.44 | 477.52 |
| 1012.52 | 472.95 |
| 1003.92 | 472.54 |
| 1015.85 | 469.17 |
| 1011.09 | 435.21 |
| 1004.81 | 477.78 |
| 1012.86 | 475.89 |
| 1017.75 | 483.9 |
| 1016.53 | 476.2 |
| 1015.47 | 462.16 |
| 1011.24 | 471.05 |
| 1010.6 | 484.71 |
| 1015.43 | 446.34 |
| 1007.21 | 469.02 |
| 1004.23 | 432.12 |
| 1015.51 | 467.28 |
| 1013.29 | 429.66 |
| 1013.16 | 469.49 |
| 1023.23 | 485.87 |
| 1026.0 | 481.95 |
| 1031.1 | 479.03 |
| 1010.99 | 434.5 |
| 1015.16 | 464.9 |
| 1020.02 | 452.71 |
| 1010.84 | 429.74 |
| 1020.67 | 457.09 |
| 1011.61 | 446.77 |
| 1014.46 | 460.76 |
| 1008.31 | 471.95 |
| 1014.09 | 453.29 |
| 1016.34 | 441.61 |
| 1017.01 | 464.73 |
| 1016.91 | 464.68 |
| 1003.72 | 430.59 |
| 1014.19 | 438.01 |
| 1021.84 | 479.08 |
| 1009.28 | 436.39 |
| 1016.25 | 447.07 |
| 1011.9 | 479.91 |
| 1013.88 | 489.05 |
| 1016.46 | 463.17 |
| 1001.18 | 471.26 |
| 1011.64 | 480.49 |
| 1025.41 | 473.78 |
| 1016.76 | 455.5 |
| 1013.78 | 446.27 |
| 1021.21 | 482.2 |
| 1016.72 | 452.48 |
| 1011.92 | 464.48 |
| 1015.29 | 438.1 |
| 1012.77 | 445.6 |
| 1010.37 | 442.43 |
| 1011.56 | 436.67 |
| 1016.54 | 466.56 |
| 1019.65 | 457.29 |
| 1029.65 | 487.03 |
| 1026.45 | 464.93 |
| 1019.28 | 466.0 |
| 1017.44 | 469.52 |
| 1011.18 | 428.88 |
| 1018.49 | 474.3 |
| 1009.69 | 461.06 |
| 1014.09 | 465.57 |
| 1012.3 | 467.67 |
| 1016.02 | 466.99 |
| 1013.73 | 463.72 |
| 1004.03 | 443.78 |
| 1005.43 | 445.23 |
| 1017.13 | 464.43 |
| 1028.31 | 484.36 |
| 1012.5 | 442.16 |
| 1004.64 | 464.11 |
| 1018.35 | 462.48 |
| 1009.59 | 477.49 |
| 1012.78 | 437.04 |
| 1003.8 | 457.09 |
| 1012.22 | 450.6 |
| 1023.25 | 465.78 |
| 1010.15 | 427.1 |
| 1020.14 | 459.81 |
| 1006.64 | 447.36 |
| 1011.0 | 488.92 |
| 1012.96 | 433.36 |
| 1021.99 | 483.35 |
| 1026.57 | 469.53 |
| 1012.27 | 476.96 |
| 1017.5 | 440.75 |
| 1024.51 | 462.55 |
| 1012.05 | 448.04 |
| 1016.22 | 455.24 |
| 1011.8 | 494.75 |
| 1012.55 | 444.58 |
| 1011.56 | 484.82 |
| 1019.6 | 442.9 |
| 1009.68 | 485.46 |
| 1012.82 | 457.81 |
| 1013.85 | 481.92 |
| 1009.63 | 443.23 |
| 1000.91 | 474.29 |
| 1009.98 | 430.46 |
| 1013.56 | 455.71 |
| 1011.25 | 438.34 |
| 1003.24 | 485.83 |
| 1017.76 | 452.82 |
| 1007.68 | 435.04 |
| 1016.82 | 451.21 |
| 1028.41 | 465.81 |
| 1025.04 | 458.42 |
| 1026.09 | 470.22 |
| 1020.84 | 449.24 |
| 1023.84 | 471.43 |
| 1023.74 | 473.26 |
| 1011.7 | 452.82 |
| 1008.1 | 432.69 |
| 1017.91 | 444.13 |
| 1029.8 | 467.21 |
| 1002.33 | 445.98 |
| 1002.42 | 436.91 |
| 1009.05 | 455.01 |
| 1002.47 | 437.11 |
| 1020.68 | 477.06 |
| 1006.65 | 441.71 |
| 1019.63 | 495.76 |
| 1011.33 | 445.63 |
| 1012.88 | 464.72 |
| 1005.94 | 438.03 |
| 1003.47 | 434.78 |
| 1015.63 | 444.67 |
| 1012.2 | 452.24 |
| 1014.19 | 450.92 |
| 1006.65 | 436.53 |
| 1005.75 | 435.53 |
| 1013.23 | 440.01 |
| 1008.72 | 443.1 |
| 1007.18 | 427.49 |
| 1012.99 | 436.25 |
| 1009.99 | 440.74 |
| 1015.02 | 443.54 |
| 1010.82 | 459.42 |
| 1009.76 | 439.66 |
| 1023.55 | 464.15 |
| 1020.55 | 459.1 |
| 1014.76 | 455.68 |
| 1015.33 | 469.08 |
| 1007.71 | 478.02 |
| 1017.36 | 456.8 |
| 1009.18 | 441.13 |
| 1017.05 | 463.88 |
| 1006.14 | 430.45 |
| 1014.24 | 449.18 |
| 1010.92 | 447.89 |
| 1010.4 | 431.59 |
| 1009.36 | 447.5 |
| 1033.04 | 475.58 |
| 1016.77 | 453.24 |
| 1012.59 | 446.4 |
| 1025.1 | 476.81 |
| 1019.29 | 474.1 |
| 1016.66 | 450.71 |
| 1006.4 | 433.62 |
| 1011.45 | 465.14 |
| 1019.08 | 445.18 |
| 1015.3 | 474.12 |
| 1016.08 | 483.91 |
| 1010.55 | 486.68 |
| 1022.43 | 464.98 |
| 1010.83 | 481.4 |
| 1021.81 | 479.2 |
| 1005.85 | 463.86 |
| 1012.76 | 472.3 |
| 1001.31 | 446.51 |
| 1005.93 | 437.71 |
| 1001.96 | 458.94 |
| 1007.62 | 437.91 |
| 1009.96 | 490.76 |
| 1013.4 | 439.66 |
| 1007.58 | 463.27 |
| 1016.68 | 473.99 |
| 1012.83 | 433.38 |
| 1015.13 | 459.01 |
| 1016.05 | 471.44 |
| 1012.97 | 471.91 |
| 1028.2 | 465.15 |
| 1008.25 | 446.66 |
| 1009.78 | 438.15 |
| 1008.81 | 447.14 |
| 1025.53 | 472.32 |
| 1010.16 | 441.68 |
| 1009.33 | 440.04 |
| 1009.82 | 444.82 |
| 1014.5 | 457.26 |
| 1009.13 | 428.83 |
| 1009.93 | 449.07 |
| 1009.38 | 435.21 |
| 1017.59 | 471.03 |
| 1012.47 | 465.56 |
| 1019.86 | 442.83 |
| 1017.26 | 460.3 |
| 1023.07 | 474.25 |
| 1033.3 | 477.97 |
| 1019.1 | 472.16 |
| 1014.22 | 456.08 |
| 1014.9 | 452.41 |
| 1011.31 | 463.71 |
| 1006.26 | 433.72 |
| 1016.0 | 456.4 |
| 1015.41 | 448.43 |
| 1020.63 | 481.6 |
| 1001.16 | 457.07 |
| 1019.8 | 451.0 |
| 1018.48 | 440.28 |
| 1002.26 | 437.47 |
| 1004.07 | 443.57 |
| 1004.91 | 426.6 |
| 1013.12 | 470.87 |
| 1012.9 | 478.37 |
| 1013.32 | 453.92 |
| 1020.79 | 470.22 |
| 1011.37 | 434.54 |
| 1013.11 | 442.89 |
| 1013.29 | 479.03 |
| 1020.5 | 476.06 |
| 1022.62 | 473.88 |
| 1010.84 | 451.75 |
| 1012.68 | 439.2 |
| 1015.58 | 439.7 |
| 1013.68 | 463.6 |
| 1004.21 | 447.47 |
| 1013.23 | 447.92 |
| 1020.44 | 471.08 |
| 1007.99 | 437.55 |
| 1012.36 | 448.27 |
| 998.47 | 431.69 |
| 1016.57 | 449.09 |
| 1015.93 | 448.79 |
| 1025.21 | 460.21 |
| 1013.54 | 479.28 |
| 1032.67 | 483.11 |
| 1011.46 | 450.75 |
| 1010.43 | 437.97 |
| 1008.53 | 459.76 |
| 1020.5 | 457.75 |
| 1015.48 | 469.33 |
| 1009.74 | 433.28 |
| 1010.23 | 444.64 |
| 1021.3 | 463.1 |
| 1022.01 | 460.91 |
| 1023.95 | 479.35 |
| 1017.65 | 449.23 |
| 1021.83 | 474.51 |
| 1007.81 | 435.02 |
| 1009.43 | 435.45 |
| 1013.3 | 452.38 |
| 1019.73 | 480.41 |
| 1019.54 | 478.96 |
| 1026.58 | 468.87 |
| 1007.89 | 434.01 |
| 1013.85 | 466.36 |
| 1011.44 | 435.28 |
| 1014.51 | 486.46 |
| 1015.51 | 468.19 |
| 1022.14 | 468.37 |
| 1019.17 | 474.19 |
| 1009.52 | 440.32 |
| 1015.35 | 485.32 |
| 1014.38 | 464.27 |
| 1013.66 | 479.25 |
| 1007.0 | 430.4 |
| 1016.65 | 447.49 |
| 1006.85 | 438.23 |
| 1025.46 | 492.09 |
| 1015.13 | 475.36 |
| 1016.68 | 452.56 |
| 1015.98 | 427.84 |
| 1010.8 | 433.95 |
| 1008.48 | 435.27 |
| 1014.04 | 454.62 |
| 1020.36 | 472.17 |
| 1015.96 | 452.42 |
| 1003.19 | 472.17 |
| 1018.01 | 481.83 |
| 1021.83 | 458.78 |
| 1022.47 | 447.5 |
| 1019.04 | 463.4 |
| 1022.67 | 473.57 |
| 1009.07 | 433.72 |
| 1011.2 | 431.85 |
| 1012.13 | 433.47 |
| 1007.45 | 432.84 |
| 1007.29 | 436.6 |
| 1020.12 | 490.23 |
| 1020.58 | 477.16 |
| 1010.44 | 441.06 |
| 1007.22 | 440.86 |
| 1033.08 | 477.94 |
| 1026.56 | 474.47 |
| 1012.18 | 470.67 |
| 1013.7 | 447.31 |
| 1018.14 | 466.8 |
| 1007.4 | 430.91 |
| 1007.2 | 434.75 |
| 1010.82 | 469.52 |
| 1008.88 | 438.9 |
| 1010.51 | 429.56 |
| 1013.53 | 432.92 |
| 1005.68 | 442.87 |
| 1022.57 | 466.59 |
| 1028.04 | 479.61 |
| 1019.12 | 471.08 |
| 1016.51 | 433.37 |
| 1018.8 | 443.92 |
| 1016.74 | 443.5 |
| 1017.37 | 439.89 |
| 1008.9 | 434.66 |
| 1021.95 | 487.57 |
| 1013.76 | 464.64 |
| 1017.26 | 470.92 |
| 1014.37 | 444.39 |
| 1002.54 | 442.48 |
| 1005.25 | 449.61 |
| 1009.43 | 435.02 |
| 1021.81 | 458.67 |
| 1016.63 | 461.74 |
| 1008.09 | 438.31 |
| 1011.68 | 462.38 |
| 1019.39 | 460.56 |
| 1013.37 | 439.22 |
| 1009.71 | 444.64 |
| 999.8 | 430.34 |
| 1008.37 | 430.46 |
| 1009.02 | 456.79 |
| 994.17 | 468.82 |
| 1008.79 | 448.51 |
| 1014.67 | 470.77 |
| 1025.79 | 465.74 |
| 1005.31 | 430.21 |
| 1008.62 | 449.23 |
| 1021.91 | 461.89 |
| 1010.97 | 445.72 |
| 1013.36 | 466.13 |
| 1008.38 | 448.71 |
| 998.43 | 469.25 |
| 1013.04 | 450.56 |
| 1008.94 | 464.46 |
| 1014.87 | 471.13 |
| 1010.92 | 461.52 |
| 1012.04 | 451.09 |
| 1005.43 | 431.51 |
| 1009.65 | 469.8 |
| 1012.05 | 442.28 |
| 1011.84 | 458.67 |
| 1016.13 | 462.4 |
| 1015.18 | 453.54 |
| 1019.81 | 444.38 |
| 1003.39 | 440.52 |
| 1007.68 | 433.62 |
| 1023.68 | 481.96 |
| 1008.89 | 452.75 |
| 1026.4 | 481.28 |
| 1020.28 | 439.03 |
| 1005.64 | 435.75 |
| 1009.72 | 436.03 |
| 1011.03 | 445.6 |
| 1020.27 | 462.65 |
| 1005.87 | 438.66 |
| 1008.54 | 447.32 |
| 1004.85 | 484.55 |
| 1024.9 | 476.8 |
| 1023.99 | 480.34 |
| 1010.53 | 440.63 |
| 1014.27 | 459.48 |
| 1025.98 | 490.78 |
| 1019.25 | 483.56 |
| 1012.26 | 429.38 |
| 1013.49 | 440.27 |
| 1011.34 | 445.34 |
| 1013.86 | 447.43 |
| 1004.37 | 439.91 |
| 1016.11 | 459.27 |
| 1017.88 | 478.89 |
| 1012.41 | 466.7 |
| 1021.39 | 463.5 |
| 1015.04 | 436.21 |
| 1011.95 | 443.94 |
| 1012.87 | 439.63 |
| 1013.21 | 460.95 |
| 1015.99 | 448.69 |
| 1006.24 | 444.63 |
| 1005.49 | 473.51 |
| 1008.56 | 462.56 |
| 1011.07 | 451.76 |
| 1025.68 | 491.81 |
| 1011.04 | 429.52 |
| 1007.59 | 437.9 |
| 1003.18 | 467.54 |
| 1015.35 | 449.97 |
| 1003.61 | 436.62 |
| 1023.37 | 477.68 |
| 1016.04 | 447.26 |
| 1011.8 | 439.76 |
| 1015.23 | 437.49 |
| 1018.29 | 455.14 |
| 1021.76 | 485.5 |
| 1016.81 | 444.1 |
| 1006.91 | 432.33 |
| 1008.87 | 471.23 |
| 1017.93 | 463.89 |
| 1009.2 | 445.54 |
| 1007.99 | 446.09 |
| 1017.82 | 445.12 |
| 1018.29 | 443.31 |
| 1015.14 | 484.16 |
| 1019.86 | 477.76 |
| 1008.36 | 430.28 |
| 1010.39 | 446.48 |
| 1028.11 | 481.03 |
| 1007.41 | 466.07 |
| 1013.2 | 447.47 |
| 1019.83 | 455.93 |
| 1021.15 | 479.62 |
| 1014.28 | 455.06 |
| 1019.87 | 475.06 |
| 1012.59 | 438.89 |
| 1003.38 | 432.7 |
| 1011.89 | 452.6 |
| 1010.46 | 451.75 |
| 1008.16 | 430.66 |
| 1019.04 | 491.9 |
| 1010.04 | 439.82 |
| 1015.91 | 460.73 |
| 1015.14 | 449.7 |
| 1013.88 | 439.42 |
| 1013.33 | 439.84 |
| 1025.58 | 485.86 |
| 1011.81 | 458.1 |
| 1012.89 | 479.92 |
| 1019.94 | 458.29 |
| 1017.29 | 489.45 |
| 1012.17 | 434.0 |
| 1010.69 | 431.24 |
| 1003.26 | 439.5 |
| 1019.48 | 467.46 |
| 1010.0 | 429.27 |
| 1016.95 | 452.1 |
| 999.83 | 472.41 |
| 1002.75 | 442.14 |
| 1003.56 | 441.0 |
| 1020.76 | 463.07 |
| 1017.99 | 445.71 |
| 1017.11 | 483.16 |
| 1010.75 | 440.45 |
| 1020.6 | 481.83 |
| 1015.53 | 467.6 |
| 1004.29 | 450.88 |
| 1001.22 | 425.5 |
| 1013.95 | 451.87 |
| 1010.51 | 428.94 |
| 1002.59 | 439.86 |
| 1011.21 | 433.44 |
| 1015.12 | 438.23 |
| 1007.68 | 436.95 |
| 1021.67 | 470.19 |
| 1011.6 | 484.66 |
| 1007.56 | 430.81 |
| 1010.05 | 433.37 |
| 1014.17 | 453.02 |
| 1012.6 | 453.5 |
| 995.88 | 463.09 |
| 1016.25 | 464.56 |
| 1017.22 | 452.12 |
| 1015.66 | 470.9 |
| 1021.08 | 450.89 |
| 1009.85 | 445.04 |
| 1011.49 | 444.72 |
| 1022.07 | 460.38 |
| 1013.05 | 446.8 |
| 1018.34 | 465.05 |
| 1016.55 | 484.13 |
| 1017.01 | 488.27 |
| 1013.85 | 447.09 |
| 1017.16 | 452.02 |
| 1014.16 | 455.55 |
| 1029.41 | 480.99 |
| 1012.96 | 467.68 |
| Humidity | Power |
|---|---|
| 73.17 | 463.26 |
| 59.08 | 444.37 |
| 92.14 | 488.56 |
| 76.64 | 446.48 |
| 96.62 | 473.9 |
| 58.77 | 443.67 |
| 75.24 | 467.35 |
| 66.43 | 478.42 |
| 41.25 | 475.98 |
| 70.72 | 477.5 |
| 75.04 | 453.02 |
| 64.22 | 453.99 |
| 84.15 | 440.29 |
| 61.83 | 451.28 |
| 87.59 | 433.99 |
| 43.08 | 462.19 |
| 48.84 | 467.54 |
| 77.51 | 477.2 |
| 63.59 | 459.85 |
| 55.28 | 464.3 |
| 66.26 | 468.27 |
| 64.77 | 495.24 |
| 83.31 | 483.8 |
| 47.19 | 443.61 |
| 54.93 | 436.06 |
| 74.62 | 443.25 |
| 72.52 | 464.16 |
| 88.44 | 475.52 |
| 92.28 | 484.41 |
| 41.85 | 437.89 |
| 44.28 | 445.11 |
| 64.58 | 438.86 |
| 63.25 | 440.98 |
| 78.61 | 436.65 |
| 44.51 | 444.26 |
| 89.46 | 465.86 |
| 74.52 | 444.37 |
| 88.86 | 450.69 |
| 75.51 | 469.02 |
| 78.64 | 448.86 |
| 76.65 | 447.14 |
| 80.44 | 469.18 |
| 79.89 | 482.8 |
| 88.28 | 476.7 |
| 84.6 | 474.99 |
| 42.69 | 444.22 |
| 78.41 | 461.33 |
| 61.07 | 448.06 |
| 50.0 | 474.6 |
| 77.29 | 473.05 |
| 43.66 | 432.06 |
| 83.8 | 467.41 |
| 66.47 | 430.12 |
| 93.09 | 473.62 |
| 80.52 | 471.81 |
| 68.99 | 442.99 |
| 57.27 | 442.77 |
| 95.53 | 491.49 |
| 71.72 | 447.46 |
| 57.88 | 446.11 |
| 63.34 | 442.44 |
| 48.07 | 446.22 |
| 91.87 | 471.49 |
| 87.27 | 463.5 |
| 64.4 | 440.01 |
| 43.4 | 441.03 |
| 72.24 | 452.68 |
| 90.22 | 474.91 |
| 74.0 | 478.77 |
| 71.85 | 434.2 |
| 86.62 | 437.91 |
| 97.41 | 477.61 |
| 84.44 | 431.65 |
| 81.55 | 430.57 |
| 75.66 | 481.09 |
| 79.41 | 445.56 |
| 58.91 | 475.74 |
| 90.06 | 435.12 |
| 79.0 | 446.15 |
| 69.47 | 436.64 |
| 51.47 | 436.69 |
| 83.13 | 468.75 |
| 40.33 | 466.6 |
| 81.69 | 465.48 |
| 94.55 | 441.34 |
| 91.81 | 441.83 |
| 63.62 | 464.7 |
| 49.35 | 437.99 |
| 69.61 | 459.12 |
| 38.75 | 429.69 |
| 90.17 | 459.8 |
| 81.24 | 433.63 |
| 48.46 | 442.84 |
| 76.72 | 485.13 |
| 51.16 | 459.12 |
| 76.34 | 445.31 |
| 67.3 | 480.8 |
| 52.38 | 432.55 |
| 76.44 | 443.86 |
| 91.55 | 449.77 |
| 71.9 | 470.71 |
| 80.05 | 452.17 |
| 63.77 | 478.29 |
| 62.26 | 428.54 |
| 89.04 | 478.27 |
| 58.02 | 439.58 |
| 81.82 | 457.32 |
| 91.14 | 475.51 |
| 88.92 | 439.66 |
| 84.83 | 471.99 |
| 91.76 | 479.81 |
| 86.56 | 434.78 |
| 57.21 | 446.58 |
| 54.25 | 437.76 |
| 63.8 | 459.36 |
| 33.71 | 462.28 |
| 67.25 | 464.33 |
| 60.11 | 444.36 |
| 74.55 | 438.64 |
| 67.34 | 470.49 |
| 42.75 | 455.13 |
| 55.2 | 450.22 |
| 83.61 | 440.43 |
| 88.78 | 482.98 |
| 100.12 | 460.44 |
| 64.52 | 444.97 |
| 51.41 | 433.94 |
| 85.78 | 439.73 |
| 75.41 | 434.48 |
| 81.63 | 442.33 |
| 51.92 | 457.67 |
| 70.12 | 454.66 |
| 53.83 | 432.21 |
| 77.23 | 457.66 |
| 65.67 | 435.21 |
| 71.18 | 448.22 |
| 81.96 | 475.51 |
| 79.54 | 446.53 |
| 47.09 | 441.3 |
| 57.69 | 433.54 |
| 78.89 | 472.52 |
| 85.29 | 474.77 |
| 40.13 | 435.1 |
| 77.06 | 450.74 |
| 67.38 | 442.7 |
| 62.44 | 426.56 |
| 77.43 | 463.71 |
| 58.77 | 447.06 |
| 67.72 | 452.27 |
| 42.14 | 445.78 |
| 84.16 | 438.65 |
| 89.79 | 480.15 |
| 67.21 | 447.19 |
| 72.14 | 443.04 |
| 97.49 | 488.81 |
| 87.74 | 455.75 |
| 96.3 | 455.86 |
| 61.25 | 457.68 |
| 88.38 | 479.11 |
| 74.77 | 432.84 |
| 68.18 | 448.37 |
| 77.2 | 447.06 |
| 49.54 | 443.53 |
| 92.22 | 445.21 |
| 33.65 | 441.7 |
| 64.59 | 450.93 |
| 100.09 | 451.44 |
| 68.04 | 441.29 |
| 48.94 | 458.85 |
| 74.47 | 481.46 |
| 81.02 | 467.19 |
| 71.17 | 461.54 |
| 53.85 | 439.08 |
| 70.67 | 467.22 |
| 59.36 | 468.8 |
| 57.17 | 426.93 |
| 70.29 | 474.65 |
| 83.37 | 468.97 |
| 87.36 | 433.97 |
| 100.09 | 450.53 |
| 68.78 | 444.51 |
| 70.98 | 469.03 |
| 75.68 | 466.56 |
| 47.49 | 457.57 |
| 71.99 | 440.13 |
| 66.55 | 433.24 |
| 74.73 | 452.55 |
| 64.78 | 443.29 |
| 75.13 | 431.76 |
| 56.38 | 454.97 |
| 94.35 | 456.7 |
| 86.55 | 486.03 |
| 82.95 | 472.79 |
| 88.42 | 452.03 |
| 85.61 | 443.41 |
| 58.39 | 441.93 |
| 74.28 | 432.64 |
| 87.85 | 480.25 |
| 83.5 | 466.68 |
| 65.24 | 494.39 |
| 75.01 | 454.72 |
| 84.52 | 448.71 |
| 80.52 | 469.76 |
| 75.14 | 450.71 |
| 75.75 | 444.01 |
| 76.72 | 453.2 |
| 85.47 | 450.87 |
| 57.95 | 441.73 |
| 78.32 | 465.09 |
| 52.2 | 447.28 |
| 93.69 | 491.16 |
| 75.74 | 450.98 |
| 67.56 | 446.3 |
| 69.46 | 436.48 |
| 74.58 | 460.84 |
| 53.23 | 442.56 |
| 88.72 | 467.3 |
| 96.16 | 479.13 |
| 68.26 | 441.15 |
| 86.39 | 445.52 |
| 85.34 | 475.4 |
| 72.64 | 469.3 |
| 97.82 | 463.57 |
| 77.22 | 445.32 |
| 80.59 | 461.03 |
| 46.91 | 466.74 |
| 57.76 | 444.04 |
| 53.09 | 434.01 |
| 84.31 | 465.23 |
| 71.58 | 440.6 |
| 92.97 | 466.74 |
| 74.55 | 433.48 |
| 78.96 | 473.59 |
| 64.44 | 474.81 |
| 68.23 | 454.75 |
| 70.81 | 452.94 |
| 61.66 | 435.83 |
| 77.76 | 482.19 |
| 69.49 | 466.66 |
| 96.26 | 462.59 |
| 55.74 | 447.82 |
| 95.61 | 462.73 |
| 84.75 | 447.98 |
| 75.3 | 462.72 |
| 67.5 | 442.42 |
| 80.92 | 444.69 |
| 79.23 | 466.7 |
| 81.1 | 453.84 |
| 32.8 | 436.92 |
| 84.31 | 486.37 |
| 46.15 | 440.43 |
| 53.96 | 446.82 |
| 59.83 | 484.91 |
| 75.3 | 437.76 |
| 42.53 | 438.91 |
| 70.58 | 464.19 |
| 91.69 | 442.19 |
| 63.55 | 446.86 |
| 61.51 | 457.15 |
| 69.55 | 482.57 |
| 98.08 | 476.03 |
| 79.34 | 428.89 |
| 81.28 | 472.7 |
| 78.99 | 445.6 |
| 80.38 | 464.78 |
| 51.16 | 440.42 |
| 72.17 | 428.41 |
| 75.39 | 438.5 |
| 68.91 | 438.28 |
| 96.38 | 476.29 |
| 70.54 | 448.46 |
| 45.8 | 438.99 |
| 57.95 | 471.8 |
| 81.89 | 471.81 |
| 69.32 | 449.82 |
| 59.14 | 442.14 |
| 81.54 | 441.46 |
| 85.81 | 477.62 |
| 65.41 | 446.76 |
| 81.15 | 472.52 |
| 95.87 | 471.58 |
| 90.24 | 440.85 |
| 75.13 | 431.37 |
| 88.22 | 437.33 |
| 81.48 | 469.22 |
| 89.84 | 471.11 |
| 43.57 | 439.17 |
| 63.16 | 445.33 |
| 57.14 | 473.71 |
| 77.76 | 452.66 |
| 90.56 | 440.99 |
| 60.98 | 467.42 |
| 70.31 | 444.14 |
| 74.05 | 457.17 |
| 75.42 | 467.87 |
| 82.25 | 442.04 |
| 67.95 | 471.36 |
| 100.09 | 460.7 |
| 47.28 | 431.33 |
| 72.41 | 432.6 |
| 77.67 | 447.61 |
| 63.7 | 443.87 |
| 79.77 | 446.87 |
| 93.84 | 465.74 |
| 84.95 | 447.86 |
| 70.16 | 447.65 |
| 84.24 | 437.87 |
| 73.32 | 483.51 |
| 86.17 | 479.65 |
| 65.43 | 455.16 |
| 94.59 | 431.91 |
| 86.8 | 470.68 |
| 58.18 | 429.28 |
| 89.66 | 450.81 |
| 87.39 | 437.73 |
| 36.35 | 460.21 |
| 79.62 | 442.86 |
| 50.52 | 482.99 |
| 51.96 | 440.0 |
| 74.73 | 478.48 |
| 78.33 | 455.28 |
| 85.19 | 436.94 |
| 83.13 | 461.06 |
| 53.49 | 438.28 |
| 88.86 | 472.61 |
| 60.89 | 426.85 |
| 61.14 | 470.18 |
| 68.29 | 455.38 |
| 57.62 | 428.32 |
| 83.63 | 480.35 |
| 78.1 | 455.56 |
| 66.34 | 447.66 |
| 79.02 | 443.06 |
| 68.96 | 452.43 |
| 71.13 | 477.81 |
| 87.01 | 431.66 |
| 74.3 | 431.8 |
| 77.62 | 446.67 |
| 59.56 | 445.26 |
| 41.66 | 425.72 |
| 73.27 | 430.58 |
| 77.16 | 439.86 |
| 67.02 | 441.11 |
| 52.8 | 434.72 |
| 39.04 | 434.01 |
| 65.47 | 475.64 |
| 74.32 | 460.44 |
| 69.22 | 436.4 |
| 93.88 | 461.03 |
| 69.83 | 479.08 |
| 84.11 | 435.76 |
| 78.65 | 460.14 |
| 69.31 | 442.2 |
| 70.3 | 447.69 |
| 68.23 | 431.15 |
| 71.76 | 445.0 |
| 85.88 | 431.59 |
| 71.09 | 467.22 |
| 52.67 | 445.33 |
| 89.68 | 470.57 |
| 73.66 | 473.77 |
| 58.94 | 447.67 |
| 87.05 | 474.29 |
| 67.0 | 437.14 |
| 43.18 | 432.56 |
| 80.62 | 459.14 |
| 59.72 | 446.19 |
| 72.1 | 428.1 |
| 69.15 | 468.46 |
| 55.66 | 435.02 |
| 61.19 | 445.52 |
| 74.62 | 462.69 |
| 73.35 | 455.75 |
| 68.85 | 463.74 |
| 39.89 | 439.79 |
| 53.16 | 443.26 |
| 52.97 | 432.04 |
| 79.87 | 465.86 |
| 84.09 | 465.6 |
| 100.15 | 469.43 |
| 79.77 | 440.75 |
| 88.99 | 481.32 |
| 76.14 | 479.87 |
| 69.13 | 458.59 |
| 93.03 | 438.62 |
| 77.92 | 445.59 |
| 74.89 | 481.87 |
| 88.7 | 475.01 |
| 62.94 | 436.54 |
| 89.62 | 456.63 |
| 81.04 | 451.69 |
| 94.53 | 463.04 |
| 64.02 | 446.1 |
| 70.57 | 438.67 |
| 70.32 | 466.88 |
| 84.86 | 444.6 |
| 81.41 | 440.26 |
| 89.45 | 483.92 |
| 82.71 | 475.19 |
| 93.93 | 479.24 |
| 70.6 | 434.92 |
| 87.68 | 454.16 |
| 87.58 | 447.58 |
| 74.4 | 467.9 |
| 67.35 | 426.29 |
| 63.61 | 447.02 |
| 76.89 | 455.85 |
| 78.08 | 476.46 |
| 69.17 | 437.48 |
| 53.31 | 452.77 |
| 93.32 | 491.54 |
| 42.47 | 438.41 |
| 82.58 | 476.1 |
| 94.59 | 464.58 |
| 86.31 | 467.74 |
| 72.57 | 442.12 |
| 80.76 | 453.34 |
| 71.93 | 425.29 |
| 47.54 | 449.63 |
| 95.72 | 462.88 |
| 77.03 | 464.67 |
| 80.49 | 489.96 |
| 77.67 | 482.38 |
| 78.72 | 437.95 |
| 58.77 | 429.2 |
| 74.8 | 453.34 |
| 51.34 | 442.47 |
| 90.41 | 462.6 |
| 91.1 | 478.79 |
| 62.57 | 456.11 |
| 84.27 | 450.33 |
| 42.93 | 434.83 |
| 40.96 | 433.43 |
| 76.53 | 456.02 |
| 69.74 | 485.23 |
| 74.99 | 473.57 |
| 70.45 | 469.94 |
| 91.49 | 452.07 |
| 88.97 | 475.32 |
| 89.13 | 480.69 |
| 46.52 | 444.01 |
| 60.55 | 465.17 |
| 88.71 | 480.61 |
| 89.15 | 476.04 |
| 83.02 | 441.76 |
| 75.19 | 428.24 |
| 87.35 | 444.77 |
| 85.66 | 463.1 |
| 91.66 | 470.5 |
| 63.47 | 431.0 |
| 72.25 | 430.68 |
| 70.58 | 436.42 |
| 60.1 | 452.33 |
| 89.29 | 440.16 |
| 67.43 | 435.75 |
| 67.58 | 449.74 |
| 70.8 | 430.73 |
| 63.62 | 432.75 |
| 66.68 | 446.79 |
| 90.76 | 486.35 |
| 75.34 | 453.18 |
| 59.77 | 458.31 |
| 80.79 | 480.26 |
| 74.1 | 448.65 |
| 41.34 | 458.41 |
| 58.78 | 435.39 |
| 97.78 | 450.21 |
| 74.85 | 459.59 |
| 69.84 | 445.84 |
| 75.36 | 441.08 |
| 85.8 | 467.33 |
| 90.11 | 444.19 |
| 61.63 | 432.96 |
| 44.76 | 438.09 |
| 89.7 | 467.9 |
| 72.51 | 475.72 |
| 74.98 | 477.51 |
| 79.59 | 435.13 |
| 78.42 | 477.9 |
| 61.23 | 457.26 |
| 47.56 | 467.53 |
| 93.06 | 465.15 |
| 89.65 | 474.28 |
| 50.5 | 444.49 |
| 44.84 | 452.84 |
| 85.32 | 435.38 |
| 82.94 | 433.57 |
| 67.26 | 435.27 |
| 79.05 | 468.49 |
| 62.03 | 433.07 |
| 94.36 | 430.63 |
| 60.02 | 440.74 |
| 95.46 | 474.49 |
| 84.92 | 449.74 |
| 62.8 | 436.73 |
| 90.81 | 434.58 |
| 80.9 | 473.93 |
| 55.84 | 435.99 |
| 75.3 | 466.83 |
| 37.34 | 427.22 |
| 79.5 | 444.07 |
| 87.29 | 469.57 |
| 81.5 | 459.89 |
| 76.42 | 479.59 |
| 75.75 | 440.92 |
| 84.95 | 480.87 |
| 62.37 | 441.9 |
| 49.25 | 430.2 |
| 74.16 | 465.16 |
| 90.55 | 471.32 |
| 94.28 | 485.43 |
| 63.31 | 495.35 |
| 78.9 | 449.12 |
| 90.97 | 480.53 |
| 87.34 | 457.07 |
| 80.8 | 443.67 |
| 90.95 | 477.52 |
| 69.97 | 472.95 |
| 89.45 | 472.54 |
| 76.08 | 469.17 |
| 83.35 | 435.21 |
| 92.16 | 477.78 |
| 58.42 | 475.89 |
| 85.06 | 483.9 |
| 88.91 | 476.2 |
| 83.33 | 462.16 |
| 88.49 | 471.05 |
| 96.88 | 484.71 |
| 73.86 | 446.34 |
| 65.17 | 469.02 |
| 69.41 | 432.12 |
| 81.23 | 467.28 |
| 54.07 | 429.66 |
| 89.17 | 469.49 |
| 78.85 | 485.87 |
| 84.44 | 481.95 |
| 83.02 | 479.03 |
| 90.66 | 434.5 |
| 75.29 | 464.9 |
| 82.6 | 452.71 |
| 45.4 | 429.74 |
| 66.33 | 457.09 |
| 45.33 | 446.77 |
| 67.12 | 460.76 |
| 84.14 | 471.95 |
| 80.81 | 453.29 |
| 49.13 | 441.61 |
| 87.29 | 464.73 |
| 52.95 | 464.68 |
| 68.92 | 430.59 |
| 85.21 | 438.01 |
| 88.56 | 479.08 |
| 55.09 | 436.39 |
| 48.64 | 447.07 |
| 87.85 | 479.91 |
| 87.42 | 489.05 |
| 62.75 | 463.17 |
| 94.86 | 471.26 |
| 63.54 | 480.49 |
| 69.46 | 473.78 |
| 74.66 | 455.5 |
| 80.57 | 446.27 |
| 84.7 | 482.2 |
| 72.6 | 452.48 |
| 52.63 | 464.48 |
| 82.01 | 438.1 |
| 75.22 | 445.6 |
| 51.05 | 442.43 |
| 80.1 | 436.67 |
| 81.58 | 466.56 |
| 65.94 | 457.29 |
| 86.74 | 487.03 |
| 62.57 | 464.93 |
| 57.37 | 466.0 |
| 88.91 | 469.52 |
| 72.26 | 428.88 |
| 74.98 | 474.3 |
| 71.19 | 461.06 |
| 62.82 | 465.57 |
| 55.31 | 467.67 |
| 71.57 | 466.99 |
| 59.16 | 463.72 |
| 40.8 | 443.78 |
| 67.63 | 445.23 |
| 97.2 | 464.43 |
| 91.16 | 484.36 |
| 64.81 | 442.16 |
| 85.61 | 464.11 |
| 93.42 | 462.48 |
| 77.36 | 477.49 |
| 67.03 | 437.04 |
| 89.45 | 457.09 |
| 54.84 | 450.6 |
| 53.48 | 465.78 |
| 54.47 | 427.1 |
| 43.36 | 459.81 |
| 48.92 | 447.36 |
| 81.22 | 488.92 |
| 60.35 | 433.36 |
| 75.98 | 483.35 |
| 74.24 | 469.53 |
| 85.21 | 476.96 |
| 68.46 | 440.75 |
| 78.31 | 462.55 |
| 89.25 | 448.04 |
| 68.57 | 455.24 |
| 67.38 | 494.75 |
| 53.6 | 444.58 |
| 91.69 | 484.82 |
| 78.21 | 442.9 |
| 94.19 | 485.46 |
| 37.19 | 457.81 |
| 83.53 | 481.92 |
| 79.45 | 443.23 |
| 99.9 | 474.29 |
| 50.39 | 430.46 |
| 74.33 | 455.71 |
| 83.66 | 438.34 |
| 89.48 | 485.83 |
| 64.59 | 452.82 |
| 75.68 | 435.04 |
| 64.18 | 451.21 |
| 70.09 | 465.81 |
| 70.58 | 458.42 |
| 99.28 | 470.22 |
| 81.89 | 449.24 |
| 87.99 | 471.43 |
| 88.21 | 473.26 |
| 91.29 | 452.82 |
| 52.72 | 432.69 |
| 67.5 | 444.13 |
| 92.05 | 467.21 |
| 63.23 | 445.98 |
| 90.88 | 436.91 |
| 74.91 | 455.01 |
| 85.39 | 437.11 |
| 96.98 | 477.06 |
| 56.28 | 441.71 |
| 65.62 | 495.76 |
| 55.32 | 445.63 |
| 88.88 | 464.72 |
| 39.49 | 438.03 |
| 54.59 | 434.78 |
| 57.19 | 444.67 |
| 45.06 | 452.24 |
| 40.62 | 450.92 |
| 90.21 | 436.53 |
| 90.91 | 435.53 |
| 74.96 | 440.01 |
| 54.21 | 443.1 |
| 63.62 | 427.49 |
| 50.04 | 436.25 |
| 51.23 | 440.74 |
| 82.71 | 443.54 |
| 92.04 | 459.42 |
| 90.67 | 439.66 |
| 91.14 | 464.15 |
| 70.43 | 459.1 |
| 66.63 | 455.68 |
| 86.95 | 469.08 |
| 96.69 | 478.02 |
| 70.88 | 456.8 |
| 47.14 | 441.13 |
| 63.36 | 463.88 |
| 60.58 | 430.45 |
| 54.3 | 449.18 |
| 65.09 | 447.89 |
| 48.16 | 431.59 |
| 81.51 | 447.5 |
| 68.57 | 475.58 |
| 73.16 | 453.24 |
| 80.88 | 446.4 |
| 85.4 | 476.81 |
| 75.77 | 474.1 |
| 75.76 | 450.71 |
| 70.21 | 433.62 |
| 55.53 | 465.14 |
| 80.48 | 445.18 |
| 72.41 | 474.12 |
| 83.25 | 483.91 |
| 82.12 | 486.68 |
| 94.75 | 464.98 |
| 95.79 | 481.4 |
| 86.02 | 479.2 |
| 78.29 | 463.86 |
| 82.23 | 472.3 |
| 52.86 | 446.51 |
| 60.66 | 437.71 |
| 62.77 | 458.94 |
| 65.54 | 437.91 |
| 95.4 | 490.76 |
| 51.78 | 439.66 |
| 63.62 | 463.27 |
| 83.09 | 473.99 |
| 61.81 | 433.38 |
| 68.24 | 459.01 |
| 72.41 | 471.44 |
| 79.64 | 471.91 |
| 66.95 | 465.15 |
| 91.98 | 446.66 |
| 64.96 | 438.15 |
| 88.93 | 447.14 |
| 85.62 | 472.32 |
| 84.0 | 441.68 |
| 89.41 | 440.04 |
| 67.4 | 444.82 |
| 76.75 | 457.26 |
| 89.06 | 428.83 |
| 64.02 | 449.07 |
| 64.12 | 435.21 |
| 81.22 | 471.03 |
| 100.13 | 465.56 |
| 58.07 | 442.83 |
| 63.42 | 460.3 |
| 83.32 | 474.25 |
| 74.28 | 477.97 |
| 71.91 | 472.16 |
| 85.8 | 456.08 |
| 55.58 | 452.41 |
| 69.7 | 463.71 |
| 63.79 | 433.72 |
| 86.59 | 456.4 |
| 48.28 | 448.43 |
| 80.42 | 481.6 |
| 98.58 | 457.07 |
| 72.83 | 451.0 |
| 56.07 | 440.28 |
| 67.13 | 437.47 |
| 84.49 | 443.57 |
| 68.37 | 426.6 |
| 86.07 | 470.87 |
| 83.82 | 478.37 |
| 74.86 | 453.92 |
| 53.52 | 470.22 |
| 80.61 | 434.54 |
| 43.56 | 442.89 |
| 89.35 | 479.03 |
| 97.28 | 476.06 |
| 80.49 | 473.88 |
| 88.9 | 451.75 |
| 49.7 | 439.2 |
| 68.64 | 439.7 |
| 98.58 | 463.6 |
| 82.12 | 447.47 |
| 78.32 | 447.92 |
| 86.04 | 471.08 |
| 91.36 | 437.55 |
| 81.02 | 448.27 |
| 76.05 | 431.69 |
| 71.81 | 449.09 |
| 82.13 | 448.79 |
| 74.27 | 460.21 |
| 71.32 | 479.28 |
| 74.59 | 483.11 |
| 84.44 | 450.75 |
| 43.39 | 437.97 |
| 87.2 | 459.76 |
| 77.11 | 457.75 |
| 82.81 | 469.33 |
| 85.67 | 433.28 |
| 95.58 | 444.64 |
| 74.46 | 463.1 |
| 90.02 | 460.91 |
| 81.93 | 479.35 |
| 86.29 | 449.23 |
| 85.43 | 474.51 |
| 71.66 | 435.02 |
| 71.33 | 435.45 |
| 67.72 | 452.38 |
| 84.23 | 480.41 |
| 74.44 | 478.96 |
| 71.48 | 468.87 |
| 56.3 | 434.01 |
| 68.13 | 466.36 |
| 68.35 | 435.28 |
| 85.23 | 486.46 |
| 79.78 | 468.19 |
| 98.98 | 468.37 |
| 72.87 | 474.19 |
| 90.93 | 440.32 |
| 72.94 | 485.32 |
| 72.3 | 464.27 |
| 77.74 | 479.25 |
| 78.29 | 430.4 |
| 69.1 | 447.49 |
| 55.79 | 438.23 |
| 75.09 | 492.09 |
| 88.98 | 475.36 |
| 64.26 | 452.56 |
| 25.89 | 427.84 |
| 59.18 | 433.95 |
| 67.48 | 435.27 |
| 89.85 | 454.62 |
| 50.62 | 472.17 |
| 83.97 | 452.42 |
| 96.51 | 472.17 |
| 80.09 | 481.83 |
| 84.02 | 458.78 |
| 61.97 | 447.5 |
| 88.51 | 463.4 |
| 81.83 | 473.57 |
| 90.63 | 433.72 |
| 73.37 | 431.85 |
| 77.5 | 433.47 |
| 57.46 | 432.84 |
| 51.91 | 436.6 |
| 79.14 | 490.23 |
| 69.24 | 477.16 |
| 41.85 | 441.06 |
| 95.1 | 440.86 |
| 74.53 | 477.94 |
| 64.85 | 474.47 |
| 57.07 | 470.67 |
| 62.9 | 447.31 |
| 72.21 | 466.8 |
| 65.99 | 430.91 |
| 73.67 | 434.75 |
| 88.59 | 469.52 |
| 61.19 | 438.9 |
| 49.37 | 429.56 |
| 48.65 | 432.92 |
| 56.18 | 442.87 |
| 71.56 | 466.59 |
| 87.46 | 479.61 |
| 70.02 | 471.08 |
| 61.2 | 433.37 |
| 60.54 | 443.92 |
| 71.82 | 443.5 |
| 44.8 | 439.89 |
| 67.32 | 434.66 |
| 78.77 | 487.57 |
| 96.02 | 464.64 |
| 90.56 | 470.92 |
| 83.19 | 444.39 |
| 68.45 | 442.48 |
| 99.19 | 449.61 |
| 88.11 | 435.02 |
| 79.29 | 458.67 |
| 87.76 | 461.74 |
| 82.56 | 438.31 |
| 79.24 | 462.38 |
| 67.24 | 460.56 |
| 58.98 | 439.22 |
| 84.22 | 444.64 |
| 89.12 | 430.34 |
| 50.07 | 430.46 |
| 98.86 | 456.79 |
| 95.79 | 468.82 |
| 70.06 | 448.51 |
| 41.71 | 470.77 |
| 86.55 | 465.74 |
| 71.97 | 430.21 |
| 96.4 | 449.23 |
| 91.73 | 461.89 |
| 91.62 | 445.72 |
| 59.14 | 466.13 |
| 92.56 | 448.71 |
| 83.71 | 469.25 |
| 55.43 | 450.56 |
| 74.91 | 464.46 |
| 89.41 | 471.13 |
| 69.81 | 461.52 |
| 86.01 | 451.09 |
| 86.05 | 431.51 |
| 80.98 | 469.8 |
| 63.62 | 442.28 |
| 64.16 | 458.67 |
| 75.63 | 462.4 |
| 80.21 | 453.54 |
| 59.7 | 444.38 |
| 47.6 | 440.52 |
| 63.78 | 433.62 |
| 89.37 | 481.96 |
| 70.55 | 452.75 |
| 84.42 | 481.28 |
| 80.62 | 439.03 |
| 52.56 | 435.75 |
| 83.26 | 436.03 |
| 70.64 | 445.6 |
| 89.95 | 462.65 |
| 51.53 | 438.66 |
| 84.83 | 447.32 |
| 59.68 | 484.55 |
| 97.88 | 476.8 |
| 85.03 | 480.34 |
| 47.38 | 440.63 |
| 48.08 | 459.48 |
| 79.65 | 490.78 |
| 83.39 | 483.56 |
| 82.18 | 429.38 |
| 51.71 | 440.27 |
| 77.33 | 445.34 |
| 72.81 | 447.43 |
| 84.26 | 439.91 |
| 73.23 | 459.27 |
| 79.73 | 478.89 |
| 62.32 | 466.7 |
| 78.58 | 463.5 |
| 79.88 | 436.21 |
| 65.87 | 443.94 |
| 80.28 | 439.63 |
| 71.33 | 460.95 |
| 70.33 | 448.69 |
| 57.73 | 444.63 |
| 99.46 | 473.51 |
| 68.61 | 462.56 |
| 95.91 | 451.76 |
| 80.42 | 491.81 |
| 51.01 | 429.52 |
| 74.08 | 437.9 |
| 80.73 | 467.54 |
| 54.71 | 449.97 |
| 73.75 | 436.62 |
| 88.43 | 477.68 |
| 74.66 | 447.26 |
| 70.04 | 439.76 |
| 74.64 | 437.49 |
| 85.11 | 455.14 |
| 82.97 | 485.5 |
| 55.59 | 444.1 |
| 49.9 | 432.33 |
| 89.99 | 471.23 |
| 91.61 | 463.89 |
| 82.95 | 445.54 |
| 92.62 | 446.09 |
| 59.64 | 445.12 |
| 63.0 | 443.31 |
| 85.38 | 484.16 |
| 85.23 | 477.76 |
| 52.08 | 430.28 |
| 38.05 | 446.48 |
| 71.98 | 481.03 |
| 90.66 | 466.07 |
| 83.14 | 447.47 |
| 65.22 | 455.93 |
| 91.67 | 479.62 |
| 66.04 | 455.06 |
| 78.19 | 475.06 |
| 54.47 | 438.89 |
| 67.26 | 432.7 |
| 72.56 | 452.6 |
| 82.15 | 451.75 |
| 86.32 | 430.66 |
| 88.17 | 491.9 |
| 72.78 | 439.82 |
| 69.58 | 460.73 |
| 69.86 | 449.7 |
| 65.37 | 439.42 |
| 52.37 | 439.84 |
| 79.63 | 485.86 |
| 83.14 | 458.1 |
| 88.25 | 479.92 |
| 55.85 | 458.29 |
| 52.55 | 489.45 |
| 62.74 | 434.0 |
| 90.08 | 431.24 |
| 54.5 | 439.5 |
| 49.88 | 467.46 |
| 48.96 | 429.27 |
| 86.77 | 452.1 |
| 96.66 | 472.41 |
| 70.84 | 442.14 |
| 83.83 | 441.0 |
| 68.22 | 463.07 |
| 82.22 | 445.71 |
| 87.9 | 483.16 |
| 66.83 | 440.45 |
| 85.36 | 481.83 |
| 60.9 | 467.6 |
| 83.51 | 450.88 |
| 52.96 | 425.5 |
| 73.02 | 451.87 |
| 43.11 | 428.94 |
| 61.41 | 439.86 |
| 65.32 | 433.44 |
| 93.68 | 438.23 |
| 42.39 | 436.95 |
| 68.18 | 470.19 |
| 89.18 | 484.66 |
| 64.79 | 430.81 |
| 43.48 | 433.37 |
| 80.4 | 453.02 |
| 72.43 | 453.5 |
| 80.0 | 463.09 |
| 45.65 | 464.56 |
| 63.02 | 452.12 |
| 74.39 | 470.9 |
| 57.77 | 450.89 |
| 76.8 | 445.04 |
| 67.39 | 444.72 |
| 73.96 | 460.38 |
| 72.75 | 446.8 |
| 71.69 | 465.05 |
| 84.98 | 484.13 |
| 87.68 | 488.27 |
| 50.36 | 447.09 |
| 68.11 | 452.02 |
| 66.27 | 455.55 |
| 89.72 | 480.99 |
| 61.07 | 467.68 |
| RH | PE |
|---|---|
| 73.17 | 463.26 |
| 59.08 | 444.37 |
| 92.14 | 488.56 |
| 76.64 | 446.48 |
| 96.62 | 473.9 |
| 58.77 | 443.67 |
| 75.24 | 467.35 |
| 66.43 | 478.42 |
| 41.25 | 475.98 |
| 70.72 | 477.5 |
| 75.04 | 453.02 |
| 64.22 | 453.99 |
| 84.15 | 440.29 |
| 61.83 | 451.28 |
| 87.59 | 433.99 |
| 43.08 | 462.19 |
| 48.84 | 467.54 |
| 77.51 | 477.2 |
| 63.59 | 459.85 |
| 55.28 | 464.3 |
| 66.26 | 468.27 |
| 64.77 | 495.24 |
| 83.31 | 483.8 |
| 47.19 | 443.61 |
| 54.93 | 436.06 |
| 74.62 | 443.25 |
| 72.52 | 464.16 |
| 88.44 | 475.52 |
| 92.28 | 484.41 |
| 41.85 | 437.89 |
| 44.28 | 445.11 |
| 64.58 | 438.86 |
| 63.25 | 440.98 |
| 78.61 | 436.65 |
| 44.51 | 444.26 |
| 89.46 | 465.86 |
| 74.52 | 444.37 |
| 88.86 | 450.69 |
| 75.51 | 469.02 |
| 78.64 | 448.86 |
| 76.65 | 447.14 |
| 80.44 | 469.18 |
| 79.89 | 482.8 |
| 88.28 | 476.7 |
| 84.6 | 474.99 |
| 42.69 | 444.22 |
| 78.41 | 461.33 |
| 61.07 | 448.06 |
| 50.0 | 474.6 |
| 77.29 | 473.05 |
| 43.66 | 432.06 |
| 83.8 | 467.41 |
| 66.47 | 430.12 |
| 93.09 | 473.62 |
| 80.52 | 471.81 |
| 68.99 | 442.99 |
| 57.27 | 442.77 |
| 95.53 | 491.49 |
| 71.72 | 447.46 |
| 57.88 | 446.11 |
| 63.34 | 442.44 |
| 48.07 | 446.22 |
| 91.87 | 471.49 |
| 87.27 | 463.5 |
| 64.4 | 440.01 |
| 43.4 | 441.03 |
| 72.24 | 452.68 |
| 90.22 | 474.91 |
| 74.0 | 478.77 |
| 71.85 | 434.2 |
| 86.62 | 437.91 |
| 97.41 | 477.61 |
| 84.44 | 431.65 |
| 81.55 | 430.57 |
| 75.66 | 481.09 |
| 79.41 | 445.56 |
| 58.91 | 475.74 |
| 90.06 | 435.12 |
| 79.0 | 446.15 |
| 69.47 | 436.64 |
| 51.47 | 436.69 |
| 83.13 | 468.75 |
| 40.33 | 466.6 |
| 81.69 | 465.48 |
| 94.55 | 441.34 |
| 91.81 | 441.83 |
| 63.62 | 464.7 |
| 49.35 | 437.99 |
| 69.61 | 459.12 |
| 38.75 | 429.69 |
| 90.17 | 459.8 |
| 81.24 | 433.63 |
| 48.46 | 442.84 |
| 76.72 | 485.13 |
| 51.16 | 459.12 |
| 76.34 | 445.31 |
| 67.3 | 480.8 |
| 52.38 | 432.55 |
| 76.44 | 443.86 |
| 91.55 | 449.77 |
| 71.9 | 470.71 |
| 80.05 | 452.17 |
| 63.77 | 478.29 |
| 62.26 | 428.54 |
| 89.04 | 478.27 |
| 58.02 | 439.58 |
| 81.82 | 457.32 |
| 91.14 | 475.51 |
| 88.92 | 439.66 |
| 84.83 | 471.99 |
| 91.76 | 479.81 |
| 86.56 | 434.78 |
| 57.21 | 446.58 |
| 54.25 | 437.76 |
| 63.8 | 459.36 |
| 33.71 | 462.28 |
| 67.25 | 464.33 |
| 60.11 | 444.36 |
| 74.55 | 438.64 |
| 67.34 | 470.49 |
| 42.75 | 455.13 |
| 55.2 | 450.22 |
| 83.61 | 440.43 |
| 88.78 | 482.98 |
| 100.12 | 460.44 |
| 64.52 | 444.97 |
| 51.41 | 433.94 |
| 85.78 | 439.73 |
| 75.41 | 434.48 |
| 81.63 | 442.33 |
| 51.92 | 457.67 |
| 70.12 | 454.66 |
| 53.83 | 432.21 |
| 77.23 | 457.66 |
| 65.67 | 435.21 |
| 71.18 | 448.22 |
| 81.96 | 475.51 |
| 79.54 | 446.53 |
| 47.09 | 441.3 |
| 57.69 | 433.54 |
| 78.89 | 472.52 |
| 85.29 | 474.77 |
| 40.13 | 435.1 |
| 77.06 | 450.74 |
| 67.38 | 442.7 |
| 62.44 | 426.56 |
| 77.43 | 463.71 |
| 58.77 | 447.06 |
| 67.72 | 452.27 |
| 42.14 | 445.78 |
| 84.16 | 438.65 |
| 89.79 | 480.15 |
| 67.21 | 447.19 |
| 72.14 | 443.04 |
| 97.49 | 488.81 |
| 87.74 | 455.75 |
| 96.3 | 455.86 |
| 61.25 | 457.68 |
| 88.38 | 479.11 |
| 74.77 | 432.84 |
| 68.18 | 448.37 |
| 77.2 | 447.06 |
| 49.54 | 443.53 |
| 92.22 | 445.21 |
| 33.65 | 441.7 |
| 64.59 | 450.93 |
| 100.09 | 451.44 |
| 68.04 | 441.29 |
| 48.94 | 458.85 |
| 74.47 | 481.46 |
| 81.02 | 467.19 |
| 71.17 | 461.54 |
| 53.85 | 439.08 |
| 70.67 | 467.22 |
| 59.36 | 468.8 |
| 57.17 | 426.93 |
| 70.29 | 474.65 |
| 83.37 | 468.97 |
| 87.36 | 433.97 |
| 100.09 | 450.53 |
| 68.78 | 444.51 |
| 70.98 | 469.03 |
| 75.68 | 466.56 |
| 47.49 | 457.57 |
| 71.99 | 440.13 |
| 66.55 | 433.24 |
| 74.73 | 452.55 |
| 64.78 | 443.29 |
| 75.13 | 431.76 |
| 56.38 | 454.97 |
| 94.35 | 456.7 |
| 86.55 | 486.03 |
| 82.95 | 472.79 |
| 88.42 | 452.03 |
| 85.61 | 443.41 |
| 58.39 | 441.93 |
| 74.28 | 432.64 |
| 87.85 | 480.25 |
| 83.5 | 466.68 |
| 65.24 | 494.39 |
| 75.01 | 454.72 |
| 84.52 | 448.71 |
| 80.52 | 469.76 |
| 75.14 | 450.71 |
| 75.75 | 444.01 |
| 76.72 | 453.2 |
| 85.47 | 450.87 |
| 57.95 | 441.73 |
| 78.32 | 465.09 |
| 52.2 | 447.28 |
| 93.69 | 491.16 |
| 75.74 | 450.98 |
| 67.56 | 446.3 |
| 69.46 | 436.48 |
| 74.58 | 460.84 |
| 53.23 | 442.56 |
| 88.72 | 467.3 |
| 96.16 | 479.13 |
| 68.26 | 441.15 |
| 86.39 | 445.52 |
| 85.34 | 475.4 |
| 72.64 | 469.3 |
| 97.82 | 463.57 |
| 77.22 | 445.32 |
| 80.59 | 461.03 |
| 46.91 | 466.74 |
| 57.76 | 444.04 |
| 53.09 | 434.01 |
| 84.31 | 465.23 |
| 71.58 | 440.6 |
| 92.97 | 466.74 |
| 74.55 | 433.48 |
| 78.96 | 473.59 |
| 64.44 | 474.81 |
| 68.23 | 454.75 |
| 70.81 | 452.94 |
| 61.66 | 435.83 |
| 77.76 | 482.19 |
| 69.49 | 466.66 |
| 96.26 | 462.59 |
| 55.74 | 447.82 |
| 95.61 | 462.73 |
| 84.75 | 447.98 |
| 75.3 | 462.72 |
| 67.5 | 442.42 |
| 80.92 | 444.69 |
| 79.23 | 466.7 |
| 81.1 | 453.84 |
| 32.8 | 436.92 |
| 84.31 | 486.37 |
| 46.15 | 440.43 |
| 53.96 | 446.82 |
| 59.83 | 484.91 |
| 75.3 | 437.76 |
| 42.53 | 438.91 |
| 70.58 | 464.19 |
| 91.69 | 442.19 |
| 63.55 | 446.86 |
| 61.51 | 457.15 |
| 69.55 | 482.57 |
| 98.08 | 476.03 |
| 79.34 | 428.89 |
| 81.28 | 472.7 |
| 78.99 | 445.6 |
| 80.38 | 464.78 |
| 51.16 | 440.42 |
| 72.17 | 428.41 |
| 75.39 | 438.5 |
| 68.91 | 438.28 |
| 96.38 | 476.29 |
| 70.54 | 448.46 |
| 45.8 | 438.99 |
| 57.95 | 471.8 |
| 81.89 | 471.81 |
| 69.32 | 449.82 |
| 59.14 | 442.14 |
| 81.54 | 441.46 |
| 85.81 | 477.62 |
| 65.41 | 446.76 |
| 81.15 | 472.52 |
| 95.87 | 471.58 |
| 90.24 | 440.85 |
| 75.13 | 431.37 |
| 88.22 | 437.33 |
| 81.48 | 469.22 |
| 89.84 | 471.11 |
| 43.57 | 439.17 |
| 63.16 | 445.33 |
| 57.14 | 473.71 |
| 77.76 | 452.66 |
| 90.56 | 440.99 |
| 60.98 | 467.42 |
| 70.31 | 444.14 |
| 74.05 | 457.17 |
| 75.42 | 467.87 |
| 82.25 | 442.04 |
| 67.95 | 471.36 |
| 100.09 | 460.7 |
| 47.28 | 431.33 |
| 72.41 | 432.6 |
| 77.67 | 447.61 |
| 63.7 | 443.87 |
| 79.77 | 446.87 |
| 93.84 | 465.74 |
| 84.95 | 447.86 |
| 70.16 | 447.65 |
| 84.24 | 437.87 |
| 73.32 | 483.51 |
| 86.17 | 479.65 |
| 65.43 | 455.16 |
| 94.59 | 431.91 |
| 86.8 | 470.68 |
| 58.18 | 429.28 |
| 89.66 | 450.81 |
| 87.39 | 437.73 |
| 36.35 | 460.21 |
| 79.62 | 442.86 |
| 50.52 | 482.99 |
| 51.96 | 440.0 |
| 74.73 | 478.48 |
| 78.33 | 455.28 |
| 85.19 | 436.94 |
| 83.13 | 461.06 |
| 53.49 | 438.28 |
| 88.86 | 472.61 |
| 60.89 | 426.85 |
| 61.14 | 470.18 |
| 68.29 | 455.38 |
| 57.62 | 428.32 |
| 83.63 | 480.35 |
| 78.1 | 455.56 |
| 66.34 | 447.66 |
| 79.02 | 443.06 |
| 68.96 | 452.43 |
| 71.13 | 477.81 |
| 87.01 | 431.66 |
| 74.3 | 431.8 |
| 77.62 | 446.67 |
| 59.56 | 445.26 |
| 41.66 | 425.72 |
| 73.27 | 430.58 |
| 77.16 | 439.86 |
| 67.02 | 441.11 |
| 52.8 | 434.72 |
| 39.04 | 434.01 |
| 65.47 | 475.64 |
| 74.32 | 460.44 |
| 69.22 | 436.4 |
| 93.88 | 461.03 |
| 69.83 | 479.08 |
| 84.11 | 435.76 |
| 78.65 | 460.14 |
| 69.31 | 442.2 |
| 70.3 | 447.69 |
| 68.23 | 431.15 |
| 71.76 | 445.0 |
| 85.88 | 431.59 |
| 71.09 | 467.22 |
| 52.67 | 445.33 |
| 89.68 | 470.57 |
| 73.66 | 473.77 |
| 58.94 | 447.67 |
| 87.05 | 474.29 |
| 67.0 | 437.14 |
| 43.18 | 432.56 |
| 80.62 | 459.14 |
| 59.72 | 446.19 |
| 72.1 | 428.1 |
| 69.15 | 468.46 |
| 55.66 | 435.02 |
| 61.19 | 445.52 |
| 74.62 | 462.69 |
| 73.35 | 455.75 |
| 68.85 | 463.74 |
| 39.89 | 439.79 |
| 53.16 | 443.26 |
| 52.97 | 432.04 |
| 79.87 | 465.86 |
| 84.09 | 465.6 |
| 100.15 | 469.43 |
| 79.77 | 440.75 |
| 88.99 | 481.32 |
| 76.14 | 479.87 |
| 69.13 | 458.59 |
| 93.03 | 438.62 |
| 77.92 | 445.59 |
| 74.89 | 481.87 |
| 88.7 | 475.01 |
| 62.94 | 436.54 |
| 89.62 | 456.63 |
| 81.04 | 451.69 |
| 94.53 | 463.04 |
| 64.02 | 446.1 |
| 70.57 | 438.67 |
| 70.32 | 466.88 |
| 84.86 | 444.6 |
| 81.41 | 440.26 |
| 89.45 | 483.92 |
| 82.71 | 475.19 |
| 93.93 | 479.24 |
| 70.6 | 434.92 |
| 87.68 | 454.16 |
| 87.58 | 447.58 |
| 74.4 | 467.9 |
| 67.35 | 426.29 |
| 63.61 | 447.02 |
| 76.89 | 455.85 |
| 78.08 | 476.46 |
| 69.17 | 437.48 |
| 53.31 | 452.77 |
| 93.32 | 491.54 |
| 42.47 | 438.41 |
| 82.58 | 476.1 |
| 94.59 | 464.58 |
| 86.31 | 467.74 |
| 72.57 | 442.12 |
| 80.76 | 453.34 |
| 71.93 | 425.29 |
| 47.54 | 449.63 |
| 95.72 | 462.88 |
| 77.03 | 464.67 |
| 80.49 | 489.96 |
| 77.67 | 482.38 |
| 78.72 | 437.95 |
| 58.77 | 429.2 |
| 74.8 | 453.34 |
| 51.34 | 442.47 |
| 90.41 | 462.6 |
| 91.1 | 478.79 |
| 62.57 | 456.11 |
| 84.27 | 450.33 |
| 42.93 | 434.83 |
| 40.96 | 433.43 |
| 76.53 | 456.02 |
| 69.74 | 485.23 |
| 74.99 | 473.57 |
| 70.45 | 469.94 |
| 91.49 | 452.07 |
| 88.97 | 475.32 |
| 89.13 | 480.69 |
| 46.52 | 444.01 |
| 60.55 | 465.17 |
| 88.71 | 480.61 |
| 89.15 | 476.04 |
| 83.02 | 441.76 |
| 75.19 | 428.24 |
| 87.35 | 444.77 |
| 85.66 | 463.1 |
| 91.66 | 470.5 |
| 63.47 | 431.0 |
| 72.25 | 430.68 |
| 70.58 | 436.42 |
| 60.1 | 452.33 |
| 89.29 | 440.16 |
| 67.43 | 435.75 |
| 67.58 | 449.74 |
| 70.8 | 430.73 |
| 63.62 | 432.75 |
| 66.68 | 446.79 |
| 90.76 | 486.35 |
| 75.34 | 453.18 |
| 59.77 | 458.31 |
| 80.79 | 480.26 |
| 74.1 | 448.65 |
| 41.34 | 458.41 |
| 58.78 | 435.39 |
| 97.78 | 450.21 |
| 74.85 | 459.59 |
| 69.84 | 445.84 |
| 75.36 | 441.08 |
| 85.8 | 467.33 |
| 90.11 | 444.19 |
| 61.63 | 432.96 |
| 44.76 | 438.09 |
| 89.7 | 467.9 |
| 72.51 | 475.72 |
| 74.98 | 477.51 |
| 79.59 | 435.13 |
| 78.42 | 477.9 |
| 61.23 | 457.26 |
| 47.56 | 467.53 |
| 93.06 | 465.15 |
| 89.65 | 474.28 |
| 50.5 | 444.49 |
| 44.84 | 452.84 |
| 85.32 | 435.38 |
| 82.94 | 433.57 |
| 67.26 | 435.27 |
| 79.05 | 468.49 |
| 62.03 | 433.07 |
| 94.36 | 430.63 |
| 60.02 | 440.74 |
| 95.46 | 474.49 |
| 84.92 | 449.74 |
| 62.8 | 436.73 |
| 90.81 | 434.58 |
| 80.9 | 473.93 |
| 55.84 | 435.99 |
| 75.3 | 466.83 |
| 37.34 | 427.22 |
| 79.5 | 444.07 |
| 87.29 | 469.57 |
| 81.5 | 459.89 |
| 76.42 | 479.59 |
| 75.75 | 440.92 |
| 84.95 | 480.87 |
| 62.37 | 441.9 |
| 49.25 | 430.2 |
| 74.16 | 465.16 |
| 90.55 | 471.32 |
| 94.28 | 485.43 |
| 63.31 | 495.35 |
| 78.9 | 449.12 |
| 90.97 | 480.53 |
| 87.34 | 457.07 |
| 80.8 | 443.67 |
| 90.95 | 477.52 |
| 69.97 | 472.95 |
| 89.45 | 472.54 |
| 76.08 | 469.17 |
| 83.35 | 435.21 |
| 92.16 | 477.78 |
| 58.42 | 475.89 |
| 85.06 | 483.9 |
| 88.91 | 476.2 |
| 83.33 | 462.16 |
| 88.49 | 471.05 |
| 96.88 | 484.71 |
| 73.86 | 446.34 |
| 65.17 | 469.02 |
| 69.41 | 432.12 |
| 81.23 | 467.28 |
| 54.07 | 429.66 |
| 89.17 | 469.49 |
| 78.85 | 485.87 |
| 84.44 | 481.95 |
| 83.02 | 479.03 |
| 90.66 | 434.5 |
| 75.29 | 464.9 |
| 82.6 | 452.71 |
| 45.4 | 429.74 |
| 66.33 | 457.09 |
| 45.33 | 446.77 |
| 67.12 | 460.76 |
| 84.14 | 471.95 |
| 80.81 | 453.29 |
| 49.13 | 441.61 |
| 87.29 | 464.73 |
| 52.95 | 464.68 |
| 68.92 | 430.59 |
| 85.21 | 438.01 |
| 88.56 | 479.08 |
| 55.09 | 436.39 |
| 48.64 | 447.07 |
| 87.85 | 479.91 |
| 87.42 | 489.05 |
| 62.75 | 463.17 |
| 94.86 | 471.26 |
| 63.54 | 480.49 |
| 69.46 | 473.78 |
| 74.66 | 455.5 |
| 80.57 | 446.27 |
| 84.7 | 482.2 |
| 72.6 | 452.48 |
| 52.63 | 464.48 |
| 82.01 | 438.1 |
| 75.22 | 445.6 |
| 51.05 | 442.43 |
| 80.1 | 436.67 |
| 81.58 | 466.56 |
| 65.94 | 457.29 |
| 86.74 | 487.03 |
| 62.57 | 464.93 |
| 57.37 | 466.0 |
| 88.91 | 469.52 |
| 72.26 | 428.88 |
| 74.98 | 474.3 |
| 71.19 | 461.06 |
| 62.82 | 465.57 |
| 55.31 | 467.67 |
| 71.57 | 466.99 |
| 59.16 | 463.72 |
| 40.8 | 443.78 |
| 67.63 | 445.23 |
| 97.2 | 464.43 |
| 91.16 | 484.36 |
| 64.81 | 442.16 |
| 85.61 | 464.11 |
| 93.42 | 462.48 |
| 77.36 | 477.49 |
| 67.03 | 437.04 |
| 89.45 | 457.09 |
| 54.84 | 450.6 |
| 53.48 | 465.78 |
| 54.47 | 427.1 |
| 43.36 | 459.81 |
| 48.92 | 447.36 |
| 81.22 | 488.92 |
| 60.35 | 433.36 |
| 75.98 | 483.35 |
| 74.24 | 469.53 |
| 85.21 | 476.96 |
| 68.46 | 440.75 |
| 78.31 | 462.55 |
| 89.25 | 448.04 |
| 68.57 | 455.24 |
| 67.38 | 494.75 |
| 53.6 | 444.58 |
| 91.69 | 484.82 |
| 78.21 | 442.9 |
| 94.19 | 485.46 |
| 37.19 | 457.81 |
| 83.53 | 481.92 |
| 79.45 | 443.23 |
| 99.9 | 474.29 |
| 50.39 | 430.46 |
| 74.33 | 455.71 |
| 83.66 | 438.34 |
| 89.48 | 485.83 |
| 64.59 | 452.82 |
| 75.68 | 435.04 |
| 64.18 | 451.21 |
| 70.09 | 465.81 |
| 70.58 | 458.42 |
| 99.28 | 470.22 |
| 81.89 | 449.24 |
| 87.99 | 471.43 |
| 88.21 | 473.26 |
| 91.29 | 452.82 |
| 52.72 | 432.69 |
| 67.5 | 444.13 |
| 92.05 | 467.21 |
| 63.23 | 445.98 |
| 90.88 | 436.91 |
| 74.91 | 455.01 |
| 85.39 | 437.11 |
| 96.98 | 477.06 |
| 56.28 | 441.71 |
| 65.62 | 495.76 |
| 55.32 | 445.63 |
| 88.88 | 464.72 |
| 39.49 | 438.03 |
| 54.59 | 434.78 |
| 57.19 | 444.67 |
| 45.06 | 452.24 |
| 40.62 | 450.92 |
| 90.21 | 436.53 |
| 90.91 | 435.53 |
| 74.96 | 440.01 |
| 54.21 | 443.1 |
| 63.62 | 427.49 |
| 50.04 | 436.25 |
| 51.23 | 440.74 |
| 82.71 | 443.54 |
| 92.04 | 459.42 |
| 90.67 | 439.66 |
| 91.14 | 464.15 |
| 70.43 | 459.1 |
| 66.63 | 455.68 |
| 86.95 | 469.08 |
| 96.69 | 478.02 |
| 70.88 | 456.8 |
| 47.14 | 441.13 |
| 63.36 | 463.88 |
| 60.58 | 430.45 |
| 54.3 | 449.18 |
| 65.09 | 447.89 |
| 48.16 | 431.59 |
| 81.51 | 447.5 |
| 68.57 | 475.58 |
| 73.16 | 453.24 |
| 80.88 | 446.4 |
| 85.4 | 476.81 |
| 75.77 | 474.1 |
| 75.76 | 450.71 |
| 70.21 | 433.62 |
| 55.53 | 465.14 |
| 80.48 | 445.18 |
| 72.41 | 474.12 |
| 83.25 | 483.91 |
| 82.12 | 486.68 |
| 94.75 | 464.98 |
| 95.79 | 481.4 |
| 86.02 | 479.2 |
| 78.29 | 463.86 |
| 82.23 | 472.3 |
| 52.86 | 446.51 |
| 60.66 | 437.71 |
| 62.77 | 458.94 |
| 65.54 | 437.91 |
| 95.4 | 490.76 |
| 51.78 | 439.66 |
| 63.62 | 463.27 |
| 83.09 | 473.99 |
| 61.81 | 433.38 |
| 68.24 | 459.01 |
| 72.41 | 471.44 |
| 79.64 | 471.91 |
| 66.95 | 465.15 |
| 91.98 | 446.66 |
| 64.96 | 438.15 |
| 88.93 | 447.14 |
| 85.62 | 472.32 |
| 84.0 | 441.68 |
| 89.41 | 440.04 |
| 67.4 | 444.82 |
| 76.75 | 457.26 |
| 89.06 | 428.83 |
| 64.02 | 449.07 |
| 64.12 | 435.21 |
| 81.22 | 471.03 |
| 100.13 | 465.56 |
| 58.07 | 442.83 |
| 63.42 | 460.3 |
| 83.32 | 474.25 |
| 74.28 | 477.97 |
| 71.91 | 472.16 |
| 85.8 | 456.08 |
| 55.58 | 452.41 |
| 69.7 | 463.71 |
| 63.79 | 433.72 |
| 86.59 | 456.4 |
| 48.28 | 448.43 |
| 80.42 | 481.6 |
| 98.58 | 457.07 |
| 72.83 | 451.0 |
| 56.07 | 440.28 |
| 67.13 | 437.47 |
| 84.49 | 443.57 |
| 68.37 | 426.6 |
| 86.07 | 470.87 |
| 83.82 | 478.37 |
| 74.86 | 453.92 |
| 53.52 | 470.22 |
| 80.61 | 434.54 |
| 43.56 | 442.89 |
| 89.35 | 479.03 |
| 97.28 | 476.06 |
| 80.49 | 473.88 |
| 88.9 | 451.75 |
| 49.7 | 439.2 |
| 68.64 | 439.7 |
| 98.58 | 463.6 |
| 82.12 | 447.47 |
| 78.32 | 447.92 |
| 86.04 | 471.08 |
| 91.36 | 437.55 |
| 81.02 | 448.27 |
| 76.05 | 431.69 |
| 71.81 | 449.09 |
| 82.13 | 448.79 |
| 74.27 | 460.21 |
| 71.32 | 479.28 |
| 74.59 | 483.11 |
| 84.44 | 450.75 |
| 43.39 | 437.97 |
| 87.2 | 459.76 |
| 77.11 | 457.75 |
| 82.81 | 469.33 |
| 85.67 | 433.28 |
| 95.58 | 444.64 |
| 74.46 | 463.1 |
| 90.02 | 460.91 |
| 81.93 | 479.35 |
| 86.29 | 449.23 |
| 85.43 | 474.51 |
| 71.66 | 435.02 |
| 71.33 | 435.45 |
| 67.72 | 452.38 |
| 84.23 | 480.41 |
| 74.44 | 478.96 |
| 71.48 | 468.87 |
| 56.3 | 434.01 |
| 68.13 | 466.36 |
| 68.35 | 435.28 |
| 85.23 | 486.46 |
| 79.78 | 468.19 |
| 98.98 | 468.37 |
| 72.87 | 474.19 |
| 90.93 | 440.32 |
| 72.94 | 485.32 |
| 72.3 | 464.27 |
| 77.74 | 479.25 |
| 78.29 | 430.4 |
| 69.1 | 447.49 |
| 55.79 | 438.23 |
| 75.09 | 492.09 |
| 88.98 | 475.36 |
| 64.26 | 452.56 |
| 25.89 | 427.84 |
| 59.18 | 433.95 |
| 67.48 | 435.27 |
| 89.85 | 454.62 |
| 50.62 | 472.17 |
| 83.97 | 452.42 |
| 96.51 | 472.17 |
| 80.09 | 481.83 |
| 84.02 | 458.78 |
| 61.97 | 447.5 |
| 88.51 | 463.4 |
| 81.83 | 473.57 |
| 90.63 | 433.72 |
| 73.37 | 431.85 |
| 77.5 | 433.47 |
| 57.46 | 432.84 |
| 51.91 | 436.6 |
| 79.14 | 490.23 |
| 69.24 | 477.16 |
| 41.85 | 441.06 |
| 95.1 | 440.86 |
| 74.53 | 477.94 |
| 64.85 | 474.47 |
| 57.07 | 470.67 |
| 62.9 | 447.31 |
| 72.21 | 466.8 |
| 65.99 | 430.91 |
| 73.67 | 434.75 |
| 88.59 | 469.52 |
| 61.19 | 438.9 |
| 49.37 | 429.56 |
| 48.65 | 432.92 |
| 56.18 | 442.87 |
| 71.56 | 466.59 |
| 87.46 | 479.61 |
| 70.02 | 471.08 |
| 61.2 | 433.37 |
| 60.54 | 443.92 |
| 71.82 | 443.5 |
| 44.8 | 439.89 |
| 67.32 | 434.66 |
| 78.77 | 487.57 |
| 96.02 | 464.64 |
| 90.56 | 470.92 |
| 83.19 | 444.39 |
| 68.45 | 442.48 |
| 99.19 | 449.61 |
| 88.11 | 435.02 |
| 79.29 | 458.67 |
| 87.76 | 461.74 |
| 82.56 | 438.31 |
| 79.24 | 462.38 |
| 67.24 | 460.56 |
| 58.98 | 439.22 |
| 84.22 | 444.64 |
| 89.12 | 430.34 |
| 50.07 | 430.46 |
| 98.86 | 456.79 |
| 95.79 | 468.82 |
| 70.06 | 448.51 |
| 41.71 | 470.77 |
| 86.55 | 465.74 |
| 71.97 | 430.21 |
| 96.4 | 449.23 |
| 91.73 | 461.89 |
| 91.62 | 445.72 |
| 59.14 | 466.13 |
| 92.56 | 448.71 |
| 83.71 | 469.25 |
| 55.43 | 450.56 |
| 74.91 | 464.46 |
| 89.41 | 471.13 |
| 69.81 | 461.52 |
| 86.01 | 451.09 |
| 86.05 | 431.51 |
| 80.98 | 469.8 |
| 63.62 | 442.28 |
| 64.16 | 458.67 |
| 75.63 | 462.4 |
| 80.21 | 453.54 |
| 59.7 | 444.38 |
| 47.6 | 440.52 |
| 63.78 | 433.62 |
| 89.37 | 481.96 |
| 70.55 | 452.75 |
| 84.42 | 481.28 |
| 80.62 | 439.03 |
| 52.56 | 435.75 |
| 83.26 | 436.03 |
| 70.64 | 445.6 |
| 89.95 | 462.65 |
| 51.53 | 438.66 |
| 84.83 | 447.32 |
| 59.68 | 484.55 |
| 97.88 | 476.8 |
| 85.03 | 480.34 |
| 47.38 | 440.63 |
| 48.08 | 459.48 |
| 79.65 | 490.78 |
| 83.39 | 483.56 |
| 82.18 | 429.38 |
| 51.71 | 440.27 |
| 77.33 | 445.34 |
| 72.81 | 447.43 |
| 84.26 | 439.91 |
| 73.23 | 459.27 |
| 79.73 | 478.89 |
| 62.32 | 466.7 |
| 78.58 | 463.5 |
| 79.88 | 436.21 |
| 65.87 | 443.94 |
| 80.28 | 439.63 |
| 71.33 | 460.95 |
| 70.33 | 448.69 |
| 57.73 | 444.63 |
| 99.46 | 473.51 |
| 68.61 | 462.56 |
| 95.91 | 451.76 |
| 80.42 | 491.81 |
| 51.01 | 429.52 |
| 74.08 | 437.9 |
| 80.73 | 467.54 |
| 54.71 | 449.97 |
| 73.75 | 436.62 |
| 88.43 | 477.68 |
| 74.66 | 447.26 |
| 70.04 | 439.76 |
| 74.64 | 437.49 |
| 85.11 | 455.14 |
| 82.97 | 485.5 |
| 55.59 | 444.1 |
| 49.9 | 432.33 |
| 89.99 | 471.23 |
| 91.61 | 463.89 |
| 82.95 | 445.54 |
| 92.62 | 446.09 |
| 59.64 | 445.12 |
| 63.0 | 443.31 |
| 85.38 | 484.16 |
| 85.23 | 477.76 |
| 52.08 | 430.28 |
| 38.05 | 446.48 |
| 71.98 | 481.03 |
| 90.66 | 466.07 |
| 83.14 | 447.47 |
| 65.22 | 455.93 |
| 91.67 | 479.62 |
| 66.04 | 455.06 |
| 78.19 | 475.06 |
| 54.47 | 438.89 |
| 67.26 | 432.7 |
| 72.56 | 452.6 |
| 82.15 | 451.75 |
| 86.32 | 430.66 |
| 88.17 | 491.9 |
| 72.78 | 439.82 |
| 69.58 | 460.73 |
| 69.86 | 449.7 |
| 65.37 | 439.42 |
| 52.37 | 439.84 |
| 79.63 | 485.86 |
| 83.14 | 458.1 |
| 88.25 | 479.92 |
| 55.85 | 458.29 |
| 52.55 | 489.45 |
| 62.74 | 434.0 |
| 90.08 | 431.24 |
| 54.5 | 439.5 |
| 49.88 | 467.46 |
| 48.96 | 429.27 |
| 86.77 | 452.1 |
| 96.66 | 472.41 |
| 70.84 | 442.14 |
| 83.83 | 441.0 |
| 68.22 | 463.07 |
| 82.22 | 445.71 |
| 87.9 | 483.16 |
| 66.83 | 440.45 |
| 85.36 | 481.83 |
| 60.9 | 467.6 |
| 83.51 | 450.88 |
| 52.96 | 425.5 |
| 73.02 | 451.87 |
| 43.11 | 428.94 |
| 61.41 | 439.86 |
| 65.32 | 433.44 |
| 93.68 | 438.23 |
| 42.39 | 436.95 |
| 68.18 | 470.19 |
| 89.18 | 484.66 |
| 64.79 | 430.81 |
| 43.48 | 433.37 |
| 80.4 | 453.02 |
| 72.43 | 453.5 |
| 80.0 | 463.09 |
| 45.65 | 464.56 |
| 63.02 | 452.12 |
| 74.39 | 470.9 |
| 57.77 | 450.89 |
| 76.8 | 445.04 |
| 67.39 | 444.72 |
| 73.96 | 460.38 |
| 72.75 | 446.8 |
| 71.69 | 465.05 |
| 84.98 | 484.13 |
| 87.68 | 488.27 |
| 50.36 | 447.09 |
| 68.11 | 452.02 |
| 66.27 | 455.55 |
| 89.72 | 480.99 |
| 61.07 | 467.68 |
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
Linear Regression Model
- Linear Regression is one of the most useful work-horses of statistical learning
- See Chapter 7 of Kevin Murphy's Machine Learning froma Probabilistic Perspective for a good mathematical and algorithmic introduction.
- You should have already seen Ameet's treatment of the topic from earlier notebook.
Let's open http://spark.apache.org/docs/latest/mllib-linear-methods.html#regression for some details.
// First let's hold out 20% of our data for testing and leave 80% for training
var Array(split20, split80) = dataset.randomSplit(Array(0.20, 0.80), 1800009193L)
split20: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
split80: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
// Let's cache these datasets for performance
val testSet = split20.cache()
val trainingSet = split80.cache()
testSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
trainingSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
testSet.count() // action to actually cache
res87: Long = 1856
trainingSet.count() // action to actually cache
res88: Long = 7712
Let's take a few elements of the three DataFrames.
dataset.take(3)
res89: Array[org.apache.spark.sql.Row] = Array([14.96,41.76,1024.07,73.17,463.26], [25.18,62.96,1020.04,59.08,444.37], [5.11,39.4,1012.16,92.14,488.56])
testSet.take(3)
res90: Array[org.apache.spark.sql.Row] = Array([2.34,39.42,1028.47,69.68,490.34], [2.8,39.64,1011.01,82.96,482.66], [3.82,35.47,1016.62,84.34,489.04])
trainingSet.take(3)
res91: Array[org.apache.spark.sql.Row] = Array([1.81,39.42,1026.92,76.97,490.55], [2.58,39.42,1028.68,69.03,488.69], [2.64,39.64,1011.02,85.24,481.29])
// ***** LINEAR REGRESSION MODEL ****
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline
// Let's initialize our linear regression learner
val lr = new LinearRegression()
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline
lr: org.apache.spark.ml.regression.LinearRegression = linReg_3b8dffe4015f
// We use explain params to dump the parameters we can use
lr.explainParams()
res92: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)
The cell below is based on the Spark ML pipeline API. More information can be found in the Spark ML Programming Guide at https://spark.apache.org/docs/latest/ml-guide.html
// Now we set the parameters for the method
lr.setPredictionCol("Predicted_PE")
.setLabelCol("PE")
.setMaxIter(100)
.setRegParam(0.1)
// We will use the new spark.ml pipeline API. If you have worked with scikit-learn this will be very familiar.
val lrPipeline = new Pipeline()
lrPipeline.setStages(Array(vectorizer, lr))
// Let's first train on the entire dataset to see what we get
val lrModel = lrPipeline.fit(trainingSet)
lrPipeline: org.apache.spark.ml.Pipeline = pipeline_07136143d91d
lrModel: org.apache.spark.ml.PipelineModel = pipeline_07136143d91d
Since Linear Regression is simply a line of best fit over the data that minimizes the square of the error, given multiple input dimensions we can express each predictor as a line function of the form:
\[ y = b_0 + b_1 x_1 + b_2 x_2 + b_3 x_3 + \ldots + b_i x_i + \ldots + b_k x_k \]
where \(b_0\) is the intercept and \(b_i\)'s are coefficients.
To express the coefficients of that line we can retrieve the Estimator stage from the fitted, linear-regression pipeline model named lrModel and express the weights and the intercept for the function.
// The intercept is as follows:
val intercept = lrModel.stages(1).asInstanceOf[LinearRegressionModel].intercept
intercept: Double = 434.0183482461227
// The coefficents (i.e. weights) are as follows:
val weights = lrModel.stages(1).asInstanceOf[LinearRegressionModel].coefficients.toArray
weights: Array[Double] = Array(-1.9288465830313846, -0.24931659465920197, 0.08140785421485836, -0.1458797521995801)
The model has been fit and the intercept and coefficients displayed above.
Now, let us do some work to make a string of the model that is easy to understand for an applied data scientist or data analyst.
val featuresNoLabel = dataset.columns.filter(col => col != "PE")
featuresNoLabel: Array[String] = Array(AT, V, AP, RH)
val coefficentFeaturePairs = sc.parallelize(weights).zip(sc.parallelize(featuresNoLabel))
coefficentFeaturePairs: org.apache.spark.rdd.RDD[(Double, String)] = ZippedPartitionsRDD2[32671] at zip at command-2972105651606305:1
coefficentFeaturePairs.collect() // this just pairs each coefficient with the name of its corresponding feature
res93: Array[(Double, String)] = Array((-1.9288465830313846,AT), (-0.24931659465920197,V), (0.08140785421485836,AP), (-0.1458797521995801,RH))
// Now let's sort the coefficients from the largest to the smallest
var equation = s"y = $intercept "
//var variables = Array
coefficentFeaturePairs.sortByKey().collect().foreach({
case (weight, feature) =>
{
val symbol = if (weight > 0) "+" else "-"
val absWeight = Math.abs(weight)
equation += (s" $symbol (${absWeight} * ${feature})")
}
}
)
equation: String = y = 434.0183482461227 - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)
// Finally here is our equation
println("Linear Regression Equation: " + equation)
Linear Regression Equation: y = 434.0183482461227 - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)
Based on examining the fitted Linear Regression Equation above:
- There is a strong negative correlation between Atmospheric Temperature (AT) and Power Output due to the coefficient being greater than -1.91.
- But our other dimenensions seem to have little to no correlation with Power Output.
Do you remember Step 2: Explore Your Data? When we visualized each predictor against Power Output using a Scatter Plot, only the temperature variable seemed to have a linear correlation with Power Output so our final equation seems logical.
Now let's see what our predictions look like given this model.
val predictionsAndLabels = lrModel.transform(testSet)
display(predictionsAndLabels.select("AT", "V", "AP", "RH", "PE", "Predicted_PE"))
| AT | V | AP | RH | PE | Predicted_PE |
|---|---|---|---|---|---|
| 2.34 | 39.42 | 1028.47 | 69.68 | 490.34 | 493.23742177145215 |
| 2.8 | 39.64 | 1011.01 | 82.96 | 482.66 | 488.93663844863084 |
| 3.82 | 35.47 | 1016.62 | 84.34 | 489.04 | 488.2642491377776 |
| 3.98 | 35.47 | 1017.22 | 86.53 | 489.64 | 487.68500173970443 |
| 4.23 | 38.44 | 1016.46 | 76.64 | 489.0 | 487.8432005878593 |
| 4.32 | 35.47 | 1017.8 | 88.51 | 488.03 | 486.7875685475632 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 | 485.8682911927764 |
| 4.65 | 35.19 | 1018.23 | 94.78 | 489.36 | 485.34119715268844 |
| 4.78 | 42.85 | 1013.39 | 93.36 | 481.47 | 482.9938172155284 |
| 4.87 | 42.85 | 1012.69 | 94.72 | 482.05 | 482.5648390621137 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 | 487.00024243767876 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 | 483.3467525779819 |
| 5.01 | 39.4 | 1003.69 | 91.9 | 475.34 | 482.8336530053327 |
| 5.06 | 40.64 | 1021.49 | 93.7 | 483.73 | 483.6145343498689 |
| 5.15 | 35.19 | 1018.63 | 93.94 | 488.42 | 484.53187599470635 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 | 487.2657538698361 |
| 5.17 | 35.57 | 1026.68 | 79.86 | 491.32 | 487.10787889447494 |
| 5.21 | 41.31 | 1003.51 | 91.23 | 486.46 | 482.0547750131424 |
| 5.23 | 40.78 | 1025.13 | 92.74 | 483.12 | 483.688095258955 |
| 5.25 | 40.07 | 1019.48 | 67.7 | 495.23 | 487.0194077282659 |
| 5.28 | 45.87 | 1008.25 | 97.88 | 479.91 | 480.1986449575354 |
| 5.29 | 41.38 | 1020.62 | 83.83 | 492.12 | 484.35541367676683 |
| 5.42 | 41.38 | 1020.77 | 86.02 | 491.38 | 483.7973981417879 |
| 5.47 | 40.62 | 1018.66 | 83.61 | 481.56 | 484.07023605498495 |
| 5.51 | 41.03 | 1022.28 | 84.5 | 491.84 | 484.05572584065357 |
| 5.53 | 35.79 | 1011.19 | 94.01 | 484.64 | 483.0334383183464 |
| 5.54 | 41.38 | 1020.47 | 82.91 | 490.07 | 483.9952002249004 |
| 5.54 | 45.87 | 1008.69 | 95.91 | 478.02 | 480.02034741363497 |
| 5.56 | 45.87 | 1006.99 | 96.48 | 476.61 | 479.7602256710553 |
| 5.65 | 40.72 | 1022.46 | 85.17 | 487.09 | 483.7798894431585 |
| 5.7 | 40.62 | 1016.07 | 84.94 | 482.82 | 483.2217349280458 |
| 5.8 | 45.87 | 1009.14 | 92.06 | 481.6 | 480.1171178824119 |
| 5.82 | 40.78 | 1024.82 | 96.01 | 470.02 | 482.0478125504672 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 | 481.81327159305386 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 | 484.6333949755666 |
| 6.02 | 41.38 | 1021.2 | 88.71 | 490.57 | 482.2826790358646 |
| 6.03 | 41.17 | 1019.81 | 84.2 | 488.57 | 482.86050781997415 |
| 6.04 | 41.14 | 1027.8 | 86.4 | 480.39 | 483.17821215232124 |
| 6.04 | 41.14 | 1027.92 | 87.12 | 481.37 | 483.0829476732433 |
| 6.06 | 41.17 | 1019.67 | 84.7 | 489.62 | 482.71830544679335 |
| 6.13 | 40.64 | 1020.69 | 94.57 | 481.13 | 481.35862683823984 |
| 6.14 | 39.4 | 1011.21 | 90.87 | 485.94 | 481.4164995749685 |
| 6.15 | 40.77 | 1022.42 | 88.57 | 482.49 | 482.3037528502627 |
| 6.17 | 36.25 | 1028.68 | 90.59 | 483.77 | 483.6070229944035 |
| 6.22 | 39.33 | 1012.31 | 93.23 | 491.77 | 481.0249164343975 |
| 6.25 | 39.64 | 1010.98 | 83.45 | 483.43 | 482.2081944229683 |
| 6.3 | 41.14 | 1027.45 | 86.11 | 481.49 | 482.6905244198958 |
| 6.34 | 40.64 | 1020.62 | 94.39 | 478.78 | 480.9741288614041 |
| 6.38 | 40.07 | 1018.53 | 60.2 | 492.96 | 485.8565717694332 |
| 6.45 | 40.02 | 1032.08 | 79.7 | 481.36 | 483.99243959507345 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 | 481.76650494734884 |
| 6.49 | 43.65 | 1020.41 | 72.78 | 484.94 | 483.069724719673 |
| 6.52 | 39.85 | 1012.55 | 86.36 | 483.01 | 481.33834961288795 |
| 6.59 | 39.37 | 1020.34 | 77.92 | 488.17 | 483.1883946104104 |
| 6.65 | 39.33 | 1011.29 | 92.85 | 490.41 | 480.1679106982307 |
| 6.71 | 40.72 | 1022.78 | 80.69 | 483.11 | 482.4149038683481 |
| 6.72 | 38.91 | 1016.89 | 90.47 | 485.89 | 480.94068220101354 |
| 6.76 | 39.22 | 1013.91 | 77.0 | 487.08 | 482.508645049916 |
| 6.8 | 41.16 | 1023.17 | 95.4 | 477.8 | 480.01746628251317 |
| 6.84 | 41.06 | 1021.04 | 89.59 | 489.96 | 480.63940670945976 |
| 6.86 | 38.08 | 1019.62 | 77.37 | 486.92 | 483.0108446487773 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 | 482.57972730795177 |
| 6.91 | 39.37 | 1019.58 | 71.02 | 488.2 | 483.51586402481416 |
| 6.93 | 41.14 | 1027.18 | 84.67 | 479.06 | 481.6634377951154 |
| 6.99 | 35.19 | 1019.32 | 68.95 | 480.05 | 484.68450470880435 |
| 6.99 | 39.37 | 1020.19 | 75.06 | 487.18 | 482.8218608903564 |
| 7.05 | 43.65 | 1018.41 | 72.36 | 480.47 | 481.8880244206695 |
| 7.1 | 35.77 | 1015.39 | 92.1 | 480.36 | 480.6306788292839 |
| 7.11 | 41.74 | 1022.35 | 90.68 | 487.85 | 479.89671820679695 |
| 7.11 | 43.13 | 1018.96 | 87.82 | 486.11 | 479.6914116057231 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 | 481.1970697561745 |
| 7.3 | 43.65 | 1019.33 | 67.62 | 482.96 | 482.17217802621536 |
| 7.34 | 40.72 | 1023.01 | 80.08 | 483.92 | 481.3074409763495 |
| 7.4 | 41.04 | 1024.44 | 90.9 | 477.69 | 479.64992318380445 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 | 480.70714895561014 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 | 479.7024675196716 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 | 481.00544489343537 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 | 482.38901084355183 |
| 7.48 | 38.5 | 1014.01 | 77.35 | 488.43 | 481.25646633043965 |
| 7.52 | 41.66 | 1015.2 | 78.41 | 483.28 | 480.33371483717946 |
| 7.54 | 38.56 | 1016.49 | 69.1 | 486.76 | 482.5311759738776 |
| 7.55 | 41.04 | 1027.03 | 83.32 | 476.58 | 480.67721106043905 |
| 7.55 | 43.65 | 1019.09 | 61.71 | 481.61 | 482.53257783094546 |
| 7.57 | 41.14 | 1028.23 | 87.97 | 477.8 | 480.0330510466423 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 | 479.3324132109004 |
| 7.65 | 41.01 | 1024.31 | 97.17 | 475.89 | 478.2499419685471 |
| 7.67 | 41.66 | 1016.25 | 77.0 | 485.03 | 480.3355565472517 |
| 7.69 | 36.24 | 1013.08 | 88.37 | 482.46 | 479.7315598782737 |
| 7.7 | 40.35 | 1011.72 | 92.88 | 484.57 | 477.91894784424176 |
| 7.73 | 37.8 | 1020.71 | 63.93 | 483.94 | 483.45191519870116 |
| 7.73 | 39.04 | 1018.61 | 68.23 | 482.39 | 482.34452319301437 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 | 478.45760011663003 |
| 7.81 | 39.64 | 1011.42 | 86.68 | 482.22 | 478.7638216096892 |
| 7.82 | 40.72 | 1022.17 | 88.13 | 481.52 | 479.1388800137473 |
| 7.84 | 43.52 | 1022.23 | 96.51 | 483.79 | 477.1846287648614 |
| 7.87 | 42.85 | 1012.18 | 94.21 | 480.54 | 476.81117998099177 |
| 7.89 | 40.46 | 1019.61 | 74.53 | 477.27 | 480.84424359067077 |
| 7.9 | 40.0 | 1018.74 | 79.55 | 474.5 | 480.1364995691749 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 | 479.2235127059344 |
| 7.92 | 39.54 | 1011.51 | 84.41 | 481.44 | 478.91505388939413 |
| 7.95 | 41.26 | 1008.48 | 97.92 | 480.6 | 476.210862698602 |
| 7.97 | 38.5 | 1013.84 | 72.36 | 487.19 | 481.0254321330136 |
| 8.01 | 40.46 | 1019.42 | 76.15 | 475.42 | 480.36098930984286 |
| 8.01 | 40.62 | 1015.62 | 86.43 | 476.22 | 478.51210495606927 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 | 479.57731721621883 |
| 8.03 | 40.07 | 1016.06 | 47.46 | 488.02 | 484.33140555054337 |
| 8.04 | 40.64 | 1020.64 | 89.26 | 477.78 | 478.4450809561189 |
| 8.05 | 40.62 | 1018.16 | 73.67 | 477.2 | 480.5031526805204 |
| 8.07 | 43.41 | 1016.02 | 76.26 | 467.56 | 479.2169410835439 |
| 8.07 | 43.69 | 1017.05 | 87.34 | 485.18 | 477.6146348725092 |
| 8.11 | 41.92 | 1029.61 | 91.92 | 483.52 | 478.33312476559934 |
| 8.16 | 39.72 | 1020.54 | 82.11 | 480.21 | 479.4778900760471 |
| 8.18 | 40.02 | 1031.45 | 73.66 | 478.81 | 481.48536176155926 |
| 8.23 | 43.79 | 1016.11 | 82.11 | 484.67 | 477.9675154808001 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 | 479.6817134321857 |
| 8.25 | 41.26 | 1020.59 | 91.84 | 475.17 | 477.5050067316079 |
| 8.26 | 40.96 | 1025.23 | 89.22 | 485.73 | 478.32045063849523 |
| 8.27 | 39.64 | 1011.0 | 84.64 | 479.91 | 478.1399555772117 |
| 8.28 | 40.56 | 1023.29 | 79.44 | 486.4 | 479.65037308403333 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 | 477.1324329554068 |
| 8.3 | 36.3 | 1015.97 | 60.62 | 480.58 | 482.82343628916425 |
| 8.3 | 43.13 | 1020.02 | 83.11 | 484.07 | 478.16947013024355 |
| 8.33 | 38.08 | 1018.94 | 73.78 | 481.89 | 480.6437911412516 |
| 8.34 | 40.72 | 1023.62 | 83.75 | 483.14 | 478.8928744938167 |
| 8.35 | 43.52 | 1022.78 | 97.34 | 479.31 | 476.1246111330079 |
| 8.35 | 43.79 | 1016.2 | 85.23 | 484.21 | 477.288235770853 |
| 8.37 | 40.92 | 1021.82 | 86.03 | 476.02 | 478.30600580479216 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 | 478.2957350932866 |
| 8.42 | 42.28 | 1008.4 | 87.78 | 481.91 | 476.52270993699136 |
| 8.46 | 40.8 | 1023.57 | 81.27 | 485.06 | 478.99917896902446 |
| 8.48 | 38.5 | 1013.5 | 66.51 | 485.29 | 480.8674382556021 |
| 8.51 | 38.5 | 1013.33 | 64.28 | 482.39 | 481.1210453702997 |
| 8.61 | 43.8 | 1021.9 | 74.35 | 478.25 | 478.8354389662744 |
| 8.63 | 39.96 | 1024.39 | 99.47 | 475.79 | 476.29244393984663 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 | 479.0178844308566 |
| 8.67 | 40.77 | 1011.81 | 89.4 | 479.23 | 476.4582419334783 |
| 8.68 | 41.82 | 1032.83 | 73.62 | 478.61 | 480.1903466285615 |
| 8.72 | 36.25 | 1029.31 | 85.73 | 479.94 | 479.4487267515188 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 | 479.3384367489267 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 | 477.44189376811596 |
| 8.74 | 40.03 | 1016.81 | 93.37 | 481.07 | 476.33561360755584 |
| 8.75 | 36.3 | 1015.61 | 57.53 | 480.4 | 482.3769169335795 |
| 8.75 | 40.22 | 1008.96 | 90.53 | 482.8 | 476.04420182940044 |
| 8.76 | 41.82 | 1033.29 | 76.5 | 480.08 | 479.65335282852305 |
| 8.77 | 40.46 | 1019.68 | 77.84 | 475.0 | 478.6696951676176 |
| 8.8 | 42.32 | 1017.91 | 86.8 | 483.88 | 476.696926422392 |
| 8.81 | 43.56 | 1014.99 | 81.5 | 482.52 | 476.90393713153463 |
| 8.83 | 36.25 | 1028.86 | 85.6 | 478.45 | 479.2188844607746 |
| 8.85 | 40.22 | 1008.61 | 90.6 | 482.3 | 475.81261283946816 |
| 8.87 | 41.16 | 1023.17 | 94.13 | 472.45 | 476.2100211409317 |
| 8.88 | 36.66 | 1026.61 | 76.16 | 480.2 | 480.2141595165933 |
| 8.91 | 43.52 | 1022.78 | 98.0 | 478.38 | 474.9481764100585 |
| 8.93 | 40.46 | 1019.03 | 71.0 | 472.77 | 479.30598211413803 |
| 8.94 | 41.74 | 1022.55 | 90.74 | 483.53 | 476.3744577455605 |
| 8.96 | 40.02 | 1031.21 | 82.32 | 475.47 | 478.6980048877349 |
| 8.96 | 40.05 | 1015.91 | 89.4 | 467.24 | 476.41215657483474 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 | 480.7338755379764 |
| 9.01 | 38.56 | 1016.67 | 63.61 | 482.37 | 480.51130475015583 |
| 9.03 | 40.71 | 1025.98 | 81.94 | 484.97 | 478.0206284049 |
| 9.04 | 44.68 | 1023.14 | 90.73 | 479.53 | 475.4980717304681 |
| 9.06 | 43.79 | 1016.05 | 81.32 | 482.8 | 476.4769333498689 |
| 9.08 | 36.18 | 1020.24 | 68.37 | 477.26 | 480.5658974037096 |
| 9.08 | 36.71 | 1025.07 | 81.32 | 479.02 | 478.9378167534134 |
| 9.08 | 40.02 | 1031.2 | 75.34 | 476.69 | 479.483969889582 |
| 9.11 | 40.64 | 1020.68 | 86.91 | 476.62 | 476.72728884411293 |
| 9.12 | 41.49 | 1020.58 | 96.23 | 475.69 | 475.1283411969007 |
| 9.12 | 41.54 | 1018.61 | 79.26 | 482.95 | 477.43108128919135 |
| 9.13 | 39.04 | 1022.36 | 74.6 | 483.24 | 479.0201634085648 |
| 9.13 | 39.16 | 1014.14 | 86.17 | 484.86 | 476.63324412261045 |
| 9.14 | 37.36 | 1015.22 | 78.06 | 491.97 | 478.3337308000573 |
| 9.15 | 39.61 | 1018.11 | 75.29 | 474.88 | 478.39283560851754 |
| 9.15 | 39.61 | 1018.69 | 84.47 | 475.64 | 477.10087603877 |
| 9.16 | 41.03 | 1021.3 | 76.08 | 484.96 | 478.16396362897893 |
| 9.19 | 40.62 | 1017.78 | 68.91 | 475.42 | 478.96772021173297 |
| 9.2 | 40.03 | 1017.05 | 92.46 | 480.38 | 475.6006326388746 |
| 9.26 | 44.68 | 1023.22 | 91.44 | 478.82 | 474.9766634864767 |
| 9.29 | 39.04 | 1022.72 | 74.29 | 482.55 | 478.786077505979 |
| 9.3 | 38.18 | 1017.19 | 71.51 | 480.14 | 478.9365615888623 |
| 9.31 | 43.56 | 1015.09 | 79.96 | 482.55 | 476.1723094438278 |
| 9.32 | 37.73 | 1022.14 | 79.49 | 477.91 | 478.2490255806092 |
| 9.35 | 44.03 | 1011.02 | 88.11 | 476.25 | 474.4577268339357 |
| 9.37 | 40.11 | 1024.94 | 75.03 | 471.13 | 478.43777544278043 |
| 9.39 | 39.66 | 1019.22 | 75.33 | 482.16 | 478.00197412694763 |
| 9.41 | 34.69 | 1027.02 | 78.91 | 480.87 | 479.3152324207446 |
| 9.41 | 39.61 | 1016.14 | 78.38 | 477.34 | 477.2801935898294 |
| 9.45 | 39.04 | 1023.08 | 75.81 | 483.66 | 478.285031656868 |
| 9.46 | 42.28 | 1008.67 | 78.16 | 481.95 | 475.9420528274367 |
| 9.47 | 41.4 | 1026.49 | 87.9 | 479.68 | 476.17198214059135 |
| 9.48 | 44.68 | 1023.29 | 92.9 | 478.66 | 474.34503134979343 |
| 9.5 | 37.36 | 1015.13 | 63.41 | 478.8 | 479.7691576930105 |
| 9.51 | 43.79 | 1016.02 | 79.81 | 481.12 | 475.82678857769963 |
| 9.53 | 38.18 | 1018.43 | 75.33 | 476.54 | 478.0366119605891 |
| 9.53 | 38.38 | 1020.49 | 80.08 | 478.03 | 477.46151999839185 |
| 9.55 | 38.08 | 1019.34 | 68.38 | 479.23 | 479.110912113517 |
| 9.55 | 39.66 | 1018.8 | 74.88 | 480.74 | 477.72481326338215 |
| 9.59 | 34.69 | 1027.65 | 75.32 | 478.88 | 479.54303529435083 |
| 9.59 | 38.56 | 1017.52 | 61.89 | 481.05 | 479.71268358186353 |
| 9.61 | 44.03 | 1008.3 | 91.36 | 473.54 | 473.26068816423447 |
| 9.63 | 41.14 | 1027.99 | 87.89 | 469.73 | 476.05175958076205 |
| 9.68 | 38.16 | 1015.54 | 79.67 | 475.51 | 476.88388448180046 |
| 9.68 | 41.06 | 1022.75 | 87.44 | 476.67 | 475.61433131158714 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 | 477.7130066863276 |
| 9.72 | 41.44 | 1015.17 | 84.41 | 481.85 | 475.2673812565115 |
| 9.75 | 36.66 | 1026.21 | 70.12 | 479.45 | 479.3846135509556 |
| 9.76 | 39.16 | 1014.19 | 85.4 | 482.38 | 475.5344685772051 |
| 9.78 | 52.75 | 1022.97 | 78.31 | 469.58 | 473.85672752722735 |
| 9.82 | 39.64 | 1010.79 | 69.22 | 477.93 | 477.3826135030455 |
| 9.83 | 41.17 | 1019.34 | 72.29 | 478.21 | 477.2300569616709 |
| 9.84 | 36.66 | 1026.7 | 70.02 | 477.62 | 479.265495182268 |
| 9.86 | 37.83 | 1005.05 | 100.15 | 472.46 | 472.7773808573311 |
| 9.86 | 41.01 | 1018.49 | 98.71 | 467.37 | 473.2887424901299 |
| 9.87 | 40.81 | 1017.17 | 84.25 | 473.2 | 475.32128019247375 |
| 9.88 | 39.66 | 1017.81 | 76.05 | 480.05 | 476.83702080523557 |
| 9.92 | 40.64 | 1020.39 | 95.41 | 469.65 | 473.90133694043874 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 | 477.4312500724956 |
| 9.95 | 43.56 | 1014.85 | 82.62 | 477.88 | 474.53026960482526 |
| 9.96 | 41.26 | 1022.9 | 83.83 | 475.21 | 475.5632280329792 |
| 9.96 | 41.55 | 1002.59 | 65.86 | 475.91 | 476.45899184845075 |
| 9.96 | 42.03 | 1017.78 | 82.39 | 477.85 | 475.16451288467897 |
| 9.97 | 41.62 | 1013.99 | 96.02 | 464.86 | 472.95056743270436 |
| 9.98 | 41.01 | 1017.83 | 98.07 | 466.05 | 473.09691475779204 |
| 9.98 | 41.62 | 1013.56 | 95.77 | 463.16 | 472.93274352761154 |
| 9.99 | 41.82 | 1033.14 | 68.36 | 475.75 | 478.4561215361668 |
| 9.99 | 42.07 | 1018.78 | 85.68 | 471.6 | 474.6981382928799 |
| 10.01 | 40.78 | 1023.71 | 88.11 | 470.82 | 475.02803269176394 |
| 10.02 | 41.44 | 1017.37 | 77.65 | 479.63 | 475.85397168574394 |
| 10.06 | 34.69 | 1027.9 | 71.73 | 477.68 | 479.18053767427625 |
| 10.08 | 43.14 | 1010.67 | 85.9 | 478.73 | 473.5654621009553 |
| 10.09 | 37.14 | 1012.99 | 72.59 | 473.66 | 477.1725989266351 |
| 10.1 | 41.4 | 1024.29 | 85.94 | 474.28 | 475.0636358283201 |
| 10.1 | 41.58 | 1021.26 | 94.06 | 468.19 | 473.5875494551498 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 | 472.67682233352394 |
| 10.12 | 41.55 | 1005.78 | 62.34 | 475.46 | 476.9235641778536 |
| 10.12 | 43.72 | 1011.33 | 97.62 | 473.05 | 471.68772310073444 |
| 10.13 | 38.16 | 1013.57 | 79.04 | 474.92 | 475.9474342905188 |
| 10.15 | 41.46 | 1019.78 | 83.56 | 481.31 | 474.932278891215 |
| 10.15 | 43.41 | 1018.4 | 82.07 | 473.43 | 474.55112952359036 |
| 10.16 | 41.55 | 1005.69 | 58.04 | 477.27 | 477.46636654211125 |
| 10.18 | 43.5 | 1022.84 | 88.7 | 476.91 | 473.8650937482109 |
| 10.2 | 41.01 | 1021.39 | 96.64 | 468.27 | 473.17098851617544 |
| 10.2 | 41.46 | 1019.12 | 83.26 | 480.11 | 474.8258713039414 |
| 10.22 | 37.83 | 1005.94 | 93.53 | 471.79 | 473.1211730372522 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 | 474.62974157133897 |
| 10.24 | 39.28 | 1010.13 | 81.61 | 477.53 | 474.801072598715 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 | 471.6665106288944 |
| 10.25 | 41.46 | 1018.67 | 84.41 | 479.28 | 474.5250337253637 |
| 10.27 | 52.75 | 1026.19 | 76.78 | 470.76 | 473.3969220129792 |
| 10.28 | 39.64 | 1010.45 | 74.15 | 477.65 | 475.74847822607404 |
| 10.31 | 37.5 | 1008.55 | 99.31 | 474.16 | 472.3991408528041 |
| 10.31 | 38.18 | 1018.02 | 70.1 | 476.31 | 477.2616855096003 |
| 10.32 | 38.91 | 1012.11 | 81.49 | 479.17 | 474.9177051337058 |
| 10.33 | 40.62 | 1017.41 | 64.22 | 473.16 | 477.4228902388337 |
| 10.34 | 41.46 | 1017.75 | 89.73 | 478.03 | 473.50046202531144 |
| 10.37 | 37.83 | 1006.5 | 90.99 | 470.66 | 473.24796901874475 |
| 10.39 | 40.22 | 1006.59 | 87.77 | 479.14 | 473.09058493481064 |
| 10.4 | 42.44 | 1014.24 | 93.48 | 480.04 | 472.3076103285209 |
| 10.42 | 41.26 | 1008.48 | 96.76 | 472.54 | 471.615832151066 |
| 10.42 | 41.46 | 1021.04 | 89.16 | 479.24 | 473.69713759778955 |
| 10.44 | 37.83 | 1006.31 | 97.2 | 474.42 | 472.19156900447234 |
| 10.45 | 39.61 | 1020.23 | 73.39 | 477.41 | 476.3350912306915 |
| 10.46 | 37.5 | 1013.12 | 76.74 | 472.16 | 475.77435376625584 |
| 10.47 | 43.14 | 1010.35 | 86.86 | 476.55 | 472.6471168581127 |
| 10.51 | 37.5 | 1010.7 | 96.29 | 474.24 | 472.62895527440253 |
| 10.58 | 42.34 | 1022.32 | 94.01 | 474.91 | 472.5658087964315 |
| 10.59 | 38.38 | 1021.58 | 68.23 | 474.94 | 477.2343522450378 |
| 10.6 | 41.17 | 1018.38 | 87.92 | 478.47 | 473.38659302581107 |
| 10.6 | 41.46 | 1021.23 | 89.02 | 481.3 | 473.3858358704527 |
| 10.63 | 44.2 | 1018.63 | 90.26 | 477.19 | 472.2522916899094 |
| 10.66 | 41.93 | 1016.12 | 94.16 | 479.61 | 471.9871102146372 |
| 10.68 | 37.92 | 1009.59 | 65.05 | 474.03 | 476.66325912606675 |
| 10.7 | 44.77 | 1017.8 | 82.37 | 484.94 | 473.0585846959978 |
| 10.71 | 41.93 | 1018.23 | 90.88 | 478.76 | 472.5409240450936 |
| 10.73 | 40.35 | 1012.27 | 89.65 | 477.23 | 472.5905086170794 |
| 10.74 | 40.05 | 1015.45 | 87.33 | 477.93 | 473.2433331311532 |
| 10.76 | 44.58 | 1016.41 | 79.24 | 483.54 | 473.33367076102724 |
| 10.77 | 44.78 | 1012.87 | 84.16 | 470.66 | 472.25860679152254 |
| 10.82 | 39.96 | 1025.53 | 95.89 | 468.57 | 472.68332438968736 |
| 10.82 | 42.02 | 996.03 | 99.34 | 475.46 | 469.26491536026253 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 | 476.77045249286635 |
| 10.89 | 44.2 | 1018.3 | 86.32 | 479.16 | 472.2986932100967 |
| 10.91 | 37.92 | 1008.66 | 66.53 | 473.72 | 475.9280130742943 |
| 10.94 | 25.36 | 1009.47 | 100.1 | 470.9 | 474.1703211862971 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 | 473.8182300033406 |
| 10.94 | 40.81 | 1026.03 | 80.79 | 476.32 | 474.4834318795844 |
| 10.94 | 43.67 | 1012.36 | 73.3 | 477.34 | 473.75018039571677 |
| 10.98 | 37.5 | 1011.12 | 97.51 | 472.34 | 471.57861538146454 |
| 10.99 | 44.63 | 1020.67 | 90.09 | 473.29 | 471.6415723647869 |
| 11.01 | 43.69 | 1016.7 | 81.48 | 477.3 | 472.7701885173113 |
| 11.02 | 38.28 | 1013.51 | 95.66 | 472.11 | 471.77143688745184 |
| 11.02 | 40.0 | 1015.75 | 74.83 | 468.09 | 474.5636411763966 |
| 11.02 | 41.17 | 1018.18 | 86.86 | 477.62 | 472.7148284274264 |
| 11.04 | 39.96 | 1025.75 | 94.44 | 468.84 | 472.4884135100371 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 | 474.2579394355058 |
| 11.06 | 37.92 | 1008.76 | 67.32 | 473.16 | 475.5315818680234 |
| 11.06 | 41.16 | 1018.52 | 89.14 | 467.46 | 472.3352405654698 |
| 11.06 | 41.5 | 1013.09 | 89.8 | 476.24 | 471.7121476384473 |
| 11.1 | 40.92 | 1021.98 | 94.14 | 462.07 | 471.87019509945225 |
| 11.16 | 40.96 | 1023.49 | 83.7 | 478.3 | 473.39040211351204 |
| 11.17 | 39.72 | 1002.4 | 81.4 | 474.64 | 472.2988980097268 |
| 11.17 | 40.27 | 1009.54 | 74.56 | 476.18 | 473.7408434668035 |
| 11.18 | 37.5 | 1013.32 | 74.32 | 472.02 | 474.7548947976392 |
| 11.18 | 39.61 | 1018.56 | 68.0 | 468.75 | 475.5773739728955 |
| 11.2 | 41.38 | 1021.65 | 61.89 | 476.87 | 476.2403822241514 |
| 11.2 | 42.02 | 999.99 | 96.69 | 472.27 | 469.24091010473035 |
| 11.22 | 40.22 | 1011.01 | 81.67 | 476.7 | 472.7393314749417 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 | 472.63331852543564 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 | 471.38775711954423 |
| 11.31 | 39.61 | 1018.74 | 68.9 | 471.92 | 475.2099855538805 |
| 11.38 | 52.75 | 1026.19 | 72.71 | 469.9 | 471.84963289726664 |
| 11.41 | 39.61 | 1018.69 | 69.44 | 467.19 | 474.9342554366788 |
| 11.48 | 41.14 | 1027.67 | 90.5 | 464.07 | 472.0765967355643 |
| 11.49 | 35.76 | 1019.08 | 60.04 | 472.45 | 477.1428353332941 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 | 470.47813470324115 |
| 11.51 | 41.06 | 1021.3 | 78.11 | 476.91 | 473.32755876405025 |
| 11.53 | 41.14 | 1025.63 | 88.54 | 469.04 | 472.10000669812564 |
| 11.53 | 41.39 | 1018.39 | 85.52 | 474.49 | 471.88884153658796 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 | 471.68655893301997 |
| 11.54 | 40.77 | 1022.13 | 83.5 | 465.61 | 472.62327183365306 |
| 11.56 | 39.28 | 1011.27 | 82.05 | 477.71 | 472.2836129719507 |
| 11.56 | 41.62 | 1012.5 | 91.42 | 460.6 | 470.4334505230224 |
| 11.57 | 39.72 | 1002.26 | 78.69 | 474.72 | 471.91129640538503 |
| 11.57 | 41.54 | 1020.13 | 69.14 | 476.89 | 474.3054501914308 |
| 11.62 | 39.72 | 1018.4 | 68.06 | 471.56 | 474.6794786091428 |
| 11.67 | 37.73 | 1021.2 | 68.88 | 473.54 | 475.187496898361 |
| 11.67 | 44.6 | 1018.09 | 92.53 | 467.96 | 469.771457326924 |
| 11.68 | 40.55 | 1022.21 | 85.76 | 475.13 | 472.08490735121984 |
| 11.7 | 25.36 | 1008.65 | 92.11 | 470.88 | 473.8032225628117 |
| 11.71 | 41.44 | 1015.37 | 79.95 | 474.22 | 472.09588182193215 |
| 11.72 | 40.35 | 1012.08 | 83.98 | 476.41 | 471.4926212025492 |
| 11.76 | 41.58 | 1020.91 | 88.35 | 465.45 | 471.19014476340215 |
| 11.77 | 39.39 | 1012.9 | 85.8 | 478.51 | 471.43677609572336 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 | 469.7436046712689 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 | 471.72684171223557 |
| 11.8 | 41.54 | 1020.0 | 65.85 | 476.12 | 474.3311768410223 |
| 11.8 | 42.34 | 1020.25 | 93.54 | 466.52 | 470.11266519044227 |
| 11.8 | 43.99 | 1020.86 | 98.44 | 466.75 | 469.0361408145477 |
| 11.82 | 41.54 | 1019.96 | 67.65 | 476.97 | 474.02676004123384 |
| 11.82 | 41.67 | 1012.94 | 84.51 | 476.73 | 470.9633331252549 |
| 11.84 | 40.05 | 1014.54 | 86.78 | 473.82 | 471.12775460619287 |
| 11.86 | 39.85 | 1013.0 | 66.92 | 478.29 | 473.91084477665686 |
| 11.87 | 40.55 | 1019.06 | 94.11 | 473.79 | 470.2438958288006 |
| 11.9 | 39.16 | 1016.53 | 84.59 | 477.75 | 471.7153938676623 |
| 11.92 | 43.8 | 1022.96 | 60.49 | 470.33 | 474.55914246739445 |
| 11.93 | 38.78 | 1013.15 | 96.08 | 474.57 | 469.8009518761225 |
| 11.95 | 41.58 | 1015.83 | 89.35 | 464.57 | 470.2642322610151 |
| 11.96 | 43.41 | 1017.42 | 79.44 | 469.34 | 471.3638012594579 |
| 12.02 | 41.92 | 1030.1 | 84.45 | 465.82 | 471.92094622344274 |
| 12.02 | 43.69 | 1016.12 | 74.07 | 477.74 | 471.85580587680386 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 | 470.30107562853505 |
| 12.05 | 48.04 | 1009.14 | 100.13 | 464.14 | 466.34356012780466 |
| 12.05 | 49.83 | 1008.54 | 95.11 | 454.18 | 466.58075506687766 |
| 12.06 | 52.72 | 1024.53 | 80.05 | 467.21 | 469.3396022995035 |
| 12.07 | 38.25 | 1012.67 | 81.66 | 470.36 | 471.72756140636227 |
| 12.08 | 40.75 | 1015.84 | 86.9 | 476.84 | 470.5786344502193 |
| 12.09 | 40.6 | 1013.85 | 85.72 | 474.35 | 470.6068799512958 |
| 12.1 | 40.27 | 1005.53 | 68.37 | 477.61 | 472.5235663152981 |
| 12.1 | 41.17 | 1013.72 | 75.61 | 478.1 | 471.9097423001995 |
| 12.12 | 40.0 | 1018.72 | 84.42 | 462.1 | 471.2847044384862 |
| 12.14 | 40.83 | 1010.56 | 78.31 | 474.82 | 471.2662319288046 |
| 12.17 | 41.58 | 1013.89 | 88.98 | 463.03 | 469.73593028388524 |
| 12.19 | 40.05 | 1014.65 | 85.1 | 472.68 | 470.7066911497908 |
| 12.19 | 40.23 | 1017.45 | 82.07 | 474.91 | 471.33177180371854 |
| 12.19 | 44.63 | 1018.96 | 80.91 | 468.91 | 470.52692515963395 |
| 12.23 | 41.58 | 1018.76 | 87.66 | 464.45 | 470.2092170118331 |
| 12.24 | 49.83 | 1007.9 | 94.28 | 466.83 | 466.2832533837298 |
| 12.25 | 44.58 | 1016.47 | 81.15 | 475.24 | 470.18594349686214 |
| 12.27 | 41.17 | 1019.39 | 52.18 | 473.84 | 475.4613835085186 |
| 12.27 | 41.17 | 1019.41 | 58.1 | 475.13 | 474.5994035325814 |
| 12.3 | 39.85 | 1012.59 | 73.38 | 476.42 | 472.0863918606857 |
| 12.3 | 40.69 | 1015.74 | 82.58 | 474.46 | 470.7913069417126 |
| 12.31 | 44.03 | 1007.78 | 94.42 | 468.13 | 467.56407826412726 |
| 12.32 | 41.26 | 1022.42 | 79.74 | 470.27 | 471.5687225134983 |
| 12.32 | 43.69 | 1016.26 | 83.18 | 471.6 | 469.9595844589464 |
| 12.33 | 38.91 | 1017.24 | 79.84 | 472.49 | 471.6990473850642 |
| 12.33 | 39.85 | 1012.53 | 66.04 | 479.32 | 473.0943993730868 |
| 12.35 | 44.2 | 1017.81 | 82.49 | 471.65 | 470.00140680122996 |
| 12.36 | 40.56 | 1022.11 | 72.99 | 474.71 | 472.62554215897904 |
| 12.36 | 45.09 | 1013.05 | 88.4 | 467.2 | 468.51057584459073 |
| 12.36 | 48.04 | 1012.8 | 93.59 | 468.37 | 466.99762401287654 |
| 12.37 | 46.97 | 1013.95 | 90.76 | 464.4 | 467.7515630344035 |
| 12.38 | 45.09 | 1013.26 | 90.55 | 467.47 | 468.17545309508614 |
| 12.39 | 49.83 | 1007.6 | 92.43 | 468.43 | 466.2393815815799 |
| 12.42 | 41.58 | 1019.49 | 86.31 | 466.05 | 470.09910156010346 |
| 12.42 | 43.14 | 1015.88 | 79.48 | 471.1 | 470.4126440262426 |
| 12.43 | 43.22 | 1008.93 | 77.42 | 468.01 | 470.1081379355774 |
| 12.45 | 40.56 | 1017.84 | 66.52 | 477.41 | 473.04817642574005 |
| 12.47 | 38.25 | 1012.74 | 82.89 | 469.56 | 470.7822892277393 |
| 12.47 | 45.01 | 1017.8 | 95.25 | 467.18 | 467.70575905298347 |
| 12.49 | 41.62 | 1011.37 | 82.68 | 461.35 | 469.8226213597647 |
| 12.5 | 41.38 | 1021.87 | 57.59 | 474.4 | 474.3780743285961 |
| 12.5 | 43.67 | 1013.99 | 90.91 | 473.26 | 468.30493209232344 |
| 12.54 | 43.69 | 1017.26 | 83.59 | 470.04 | 469.5568353664925 |
| 12.55 | 39.58 | 1010.68 | 76.9 | 472.31 | 471.0025099661929 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 | 469.9361134525985 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 | 469.7928661275291 |
| 12.57 | 41.79 | 1014.99 | 87.8 | 461.88 | 469.1737219130261 |
| 12.58 | 39.72 | 1017.85 | 57.74 | 471.24 | 474.2884906123142 |
| 12.58 | 43.67 | 1014.36 | 91.72 | 473.02 | 468.06258267245875 |
| 12.59 | 39.18 | 1024.18 | 67.57 | 471.32 | 473.485146860658 |
| 12.6 | 41.74 | 1022.13 | 67.89 | 474.23 | 472.61404029065585 |
| 12.61 | 43.22 | 1013.41 | 78.94 | 466.85 | 469.903915514171 |
| 12.64 | 41.26 | 1020.79 | 83.66 | 465.78 | 470.2469481759357 |
| 12.65 | 44.34 | 1014.74 | 92.81 | 473.46 | 467.632447347929 |
| 12.68 | 41.4 | 1021.59 | 78.57 | 466.64 | 470.9425442114299 |
| 12.71 | 43.8 | 1023.15 | 71.16 | 466.2 | 471.494284203131 |
| 12.72 | 39.13 | 1008.36 | 92.59 | 467.28 | 468.30907898088435 |
| 12.72 | 40.64 | 1021.11 | 93.24 | 475.73 | 468.87573922525866 |
| 12.73 | 37.73 | 1021.89 | 61.76 | 470.89 | 474.237754775417 |
| 12.74 | 49.83 | 1007.44 | 91.47 | 466.09 | 465.69130458295615 |
| 12.75 | 39.85 | 1012.51 | 62.37 | 475.61 | 472.8180343417018 |
| 12.79 | 44.68 | 1022.51 | 99.55 | 465.75 | 466.9269506815448 |
| 12.83 | 41.67 | 1012.84 | 84.29 | 474.81 | 469.0391508364556 |
| 12.83 | 44.88 | 1017.86 | 87.88 | 474.26 | 468.1238036853617 |
| 12.87 | 39.3 | 1019.26 | 71.55 | 471.48 | 471.9340237695587 |
| 12.87 | 45.51 | 1015.3 | 86.67 | 475.77 | 467.85769076077656 |
| 12.88 | 44.0 | 1022.71 | 88.58 | 470.31 | 468.53947222591256 |
| 12.88 | 44.71 | 1018.73 | 51.95 | 469.12 | 473.38202950699997 |
| 12.89 | 36.71 | 1013.36 | 87.29 | 475.13 | 469.76472317857633 |
| 12.9 | 44.63 | 1020.72 | 89.51 | 467.41 | 468.04615604018346 |
| 12.92 | 39.35 | 1014.56 | 88.29 | 469.83 | 469.00047164404333 |
| 12.95 | 41.38 | 1021.97 | 53.83 | 474.46 | 474.0667420199239 |
| 12.99 | 40.55 | 1007.52 | 94.15 | 472.18 | 467.138305828078 |
| 13.02 | 45.51 | 1015.24 | 80.05 | 468.46 | 468.5292032616302 |
| 13.03 | 42.86 | 1014.39 | 86.25 | 475.03 | 468.1969526319267 |
| 13.04 | 40.69 | 1015.96 | 82.37 | 473.49 | 469.4125049461586 |
| 13.06 | 44.2 | 1018.95 | 85.68 | 469.02 | 468.259374271566 |
| 13.07 | 38.47 | 1015.16 | 57.26 | 468.9 | 473.50603668317063 |
| 13.07 | 40.83 | 1010.0 | 84.84 | 471.19 | 468.47422142636185 |
| 13.08 | 39.28 | 1012.41 | 77.98 | 474.13 | 470.0383017110002 |
| 13.08 | 44.9 | 1020.47 | 86.46 | 472.01 | 468.0562294553348 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 | 472.76694427363464 |
| 13.09 | 54.3 | 1017.61 | 82.38 | 471.0 | 466.0557279256278 |
| 13.1 | 40.55 | 1007.59 | 95.46 | 472.37 | 466.7407287783581 |
| 13.1 | 49.83 | 1007.29 | 92.79 | 466.08 | 464.79214736202914 |
| 13.11 | 41.44 | 1015.55 | 74.45 | 471.61 | 470.21248865654456 |
| 13.12 | 39.18 | 1023.11 | 64.33 | 471.44 | 472.8484021647681 |
| 13.15 | 40.78 | 1024.13 | 79.59 | 462.3 | 470.24854120855605 |
| 13.15 | 41.14 | 1026.72 | 80.31 | 461.49 | 470.2646001553115 |
| 13.18 | 43.72 | 1010.59 | 99.09 | 464.7 | 465.51076750880605 |
| 13.19 | 44.88 | 1017.61 | 94.95 | 467.68 | 466.3776971038656 |
| 13.2 | 40.83 | 1007.75 | 94.98 | 472.41 | 466.5610830112806 |
| 13.2 | 41.78 | 1010.49 | 64.96 | 468.58 | 470.92659992793443 |
| 13.25 | 43.7 | 1013.61 | 76.05 | 472.22 | 468.98765579029424 |
| 13.25 | 44.92 | 1024.11 | 89.34 | 469.18 | 467.5995301073336 |
| 13.27 | 40.27 | 1006.1 | 40.04 | 473.88 | 474.44599166986796 |
| 13.34 | 41.79 | 1011.48 | 86.66 | 461.12 | 467.5690713933053 |
| 13.41 | 40.89 | 1010.85 | 89.61 | 472.4 | 467.1768048505422 |
| 13.42 | 40.92 | 1022.84 | 75.89 | 458.49 | 470.12758725908657 |
| 13.43 | 43.69 | 1016.21 | 73.01 | 475.36 | 469.2980914389405 |
| 13.44 | 40.69 | 1014.54 | 77.97 | 473.0 | 469.1672380696391 |
| 13.44 | 41.14 | 1026.41 | 77.26 | 461.44 | 470.1249314556345 |
| 13.48 | 41.92 | 1030.2 | 65.96 | 463.9 | 471.81028761580865 |
| 13.49 | 43.41 | 1015.75 | 57.86 | 461.06 | 471.424799923348 |
| 13.51 | 39.31 | 1012.18 | 75.19 | 466.46 | 469.58969888462434 |
| 13.53 | 38.73 | 1004.86 | 85.38 | 472.46 | 467.6133054100996 |
| 13.53 | 42.86 | 1014.0 | 90.63 | 471.73 | 466.56182696263306 |
| 13.54 | 40.69 | 1015.85 | 77.55 | 471.05 | 469.14226719628124 |
| 13.55 | 40.71 | 1019.13 | 75.44 | 467.21 | 469.69281643752356 |
| 13.56 | 40.03 | 1017.73 | 83.76 | 472.59 | 468.5153727218602 |
| 13.56 | 49.83 | 1007.01 | 89.86 | 466.33 | 464.3095114085993 |
| 13.57 | 42.99 | 1007.51 | 88.95 | 472.04 | 466.169002951847 |
| 13.58 | 39.28 | 1016.17 | 79.17 | 472.17 | 469.2063750462149 |
| 13.61 | 38.47 | 1015.1 | 54.98 | 466.51 | 472.79218089209587 |
| 13.65 | 41.58 | 1020.56 | 72.17 | 460.8 | 469.8764663630868 |
| 13.67 | 41.48 | 1017.51 | 63.54 | 463.97 | 470.87346939701916 |
| 13.67 | 42.32 | 1015.41 | 79.04 | 464.56 | 468.2319508045607 |
| 13.69 | 34.03 | 1018.45 | 65.76 | 469.12 | 472.4449714286485 |
| 13.69 | 40.83 | 1008.53 | 84.81 | 471.26 | 467.1630433917525 |
| 13.71 | 43.41 | 1015.45 | 69.26 | 466.06 | 469.3130021437414 |
| 13.72 | 44.47 | 1027.2 | 68.89 | 470.66 | 470.0399558829108 |
| 13.72 | 49.83 | 1006.88 | 89.49 | 463.65 | 464.0442884425802 |
| 13.73 | 45.08 | 1023.55 | 81.64 | 470.35 | 467.7114787859095 |
| 13.74 | 42.74 | 1029.54 | 70.0 | 465.92 | 470.46126451393184 |
| 13.74 | 44.21 | 1023.26 | 84.2 | 466.67 | 467.51203531407947 |
| 13.77 | 42.86 | 1030.72 | 77.3 | 471.38 | 469.4046202019984 |
| 13.77 | 43.13 | 1016.63 | 62.13 | 468.45 | 470.40326389642064 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 | 469.66353144520883 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 | 465.52654943877593 |
| 13.8 | 39.82 | 1012.37 | 83.69 | 473.56 | 467.6786715108734 |
| 13.83 | 38.73 | 999.62 | 91.95 | 469.81 | 465.64964430715304 |
| 13.84 | 42.18 | 1015.74 | 79.76 | 468.66 | 467.8607823790049 |
| 13.85 | 45.08 | 1024.86 | 83.85 | 468.35 | 467.26426723260613 |
| 13.86 | 37.85 | 1011.4 | 89.7 | 469.94 | 467.0983914780623 |
| 13.86 | 40.66 | 1017.15 | 83.82 | 463.49 | 467.7236799517389 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 | 467.5049711098421 |
| 13.88 | 48.79 | 1017.28 | 79.4 | 464.14 | 466.31353063126903 |
| 13.9 | 39.54 | 1007.01 | 81.33 | 471.16 | 467.46352561567426 |
| 13.91 | 44.58 | 1021.36 | 78.98 | 472.67 | 467.6987016384138 |
| 13.93 | 42.86 | 1032.37 | 71.11 | 468.88 | 470.13332337428324 |
| 13.95 | 40.2 | 1013.18 | 90.77 | 464.79 | 466.32771593378925 |
| 13.95 | 71.14 | 1019.76 | 75.51 | 461.18 | 461.3756491943329 |
| 13.96 | 39.54 | 1011.05 | 85.72 | 468.82 | 467.03627043956425 |
| 13.97 | 45.01 | 1017.44 | 89.15 | 461.96 | 465.6730488393365 |
| 13.98 | 44.84 | 1023.6 | 89.09 | 462.81 | 466.20636936169376 |
| 14.0 | 41.78 | 1010.96 | 48.37 | 465.62 | 471.8419294419814 |
| 14.01 | 45.08 | 1023.28 | 82.49 | 464.79 | 467.025423832653 |
| 14.02 | 40.66 | 1017.05 | 84.14 | 465.39 | 467.36024219232854 |
| 14.03 | 44.88 | 1019.6 | 57.63 | 465.51 | 470.36369995609516 |
| 14.04 | 40.2 | 1013.29 | 89.54 | 465.25 | 466.34250670048556 |
| 14.04 | 40.83 | 1008.98 | 82.04 | 469.75 | 466.9286675356811 |
| 14.04 | 44.21 | 1021.93 | 86.12 | 468.64 | 466.5450197688411 |
| 14.07 | 40.78 | 1024.67 | 72.66 | 456.71 | 469.52890927618625 |
| 14.07 | 42.99 | 1007.57 | 96.05 | 468.87 | 464.17371789096717 |
| 14.07 | 43.34 | 1015.79 | 86.0 | 463.77 | 466.22172115408836 |
| 14.08 | 39.31 | 1011.67 | 72.0 | 464.16 | 468.9140947361635 |
| 14.09 | 41.2 | 1016.45 | 67.27 | 472.42 | 469.50273867747836 |
| 14.09 | 44.84 | 1023.65 | 94.29 | 466.12 | 465.2396919188332 |
| 14.1 | 41.04 | 1026.11 | 74.25 | 465.89 | 469.291500068156 |
| 14.1 | 42.18 | 1015.05 | 78.25 | 463.3 | 467.5233892738298 |
| 14.12 | 39.52 | 1016.79 | 75.89 | 472.32 | 468.6339203654876 |
| 14.12 | 41.39 | 1018.73 | 76.51 | 472.88 | 468.23518412428797 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 | 467.40072495010907 |
| 14.17 | 42.86 | 1030.94 | 66.47 | 466.2 | 470.2308690130346 |
| 14.18 | 39.3 | 1020.1 | 67.48 | 464.32 | 470.06934793478035 |
| 14.18 | 40.69 | 1014.73 | 74.88 | 471.52 | 468.2061275247934 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 | 466.5674959516581 |
| 14.22 | 42.32 | 1015.54 | 77.23 | 465.0 | 467.4457105564226 |
| 14.24 | 39.52 | 1018.22 | 77.19 | 468.51 | 468.3292283291916 |
| 14.24 | 39.99 | 1009.3 | 83.75 | 466.2 | 466.52892029567596 |
| 14.24 | 44.84 | 1023.6 | 94.06 | 466.67 | 464.97984688167367 |
| 14.27 | 41.48 | 1014.83 | 62.7 | 458.19 | 469.62052738975217 |
| 14.28 | 42.77 | 1020.06 | 81.77 | 466.75 | 466.9234567199092 |
| 14.28 | 43.02 | 1012.97 | 49.44 | 467.83 | 471.0002382734735 |
| 14.31 | 40.78 | 1024.3 | 76.41 | 463.54 | 468.4888161194508 |
| 14.32 | 45.08 | 1023.24 | 84.53 | 467.21 | 466.1266303832576 |
| 14.33 | 45.51 | 1015.42 | 71.55 | 468.92 | 467.25704554531416 |
| 14.34 | 42.86 | 1031.75 | 66.81 | 466.17 | 469.9193063400854 |
| 14.35 | 40.71 | 1023.09 | 69.5 | 473.56 | 469.33864000185486 |
| 14.35 | 49.83 | 1006.39 | 91.23 | 462.54 | 462.5353944778779 |
| 14.36 | 40.0 | 1016.16 | 68.79 | 460.42 | 469.0357845125853 |
| 14.38 | 40.66 | 1016.34 | 92.37 | 463.1 | 465.4074674853422 |
| 14.39 | 43.56 | 1012.97 | 59.17 | 469.35 | 469.2340241993221 |
| 14.4 | 40.2 | 1013.04 | 90.5 | 464.5 | 465.48772540492894 |
| 14.41 | 40.71 | 1016.78 | 69.77 | 467.01 | 468.6698381136833 |
| 14.41 | 40.83 | 1009.82 | 80.43 | 470.13 | 466.5182432985413 |
| 14.42 | 41.16 | 1004.32 | 88.42 | 467.25 | 464.80335793821706 |
| 14.43 | 35.85 | 1021.99 | 78.25 | 464.6 | 469.0300144538734 |
| 14.48 | 39.0 | 1016.75 | 75.97 | 464.56 | 468.0542535304745 |
| 14.48 | 40.89 | 1011.39 | 82.18 | 470.44 | 466.2407858068176 |
| 14.5 | 41.76 | 1023.94 | 84.42 | 464.23 | 466.68020136327283 |
| 14.52 | 40.35 | 1011.11 | 69.84 | 470.8 | 468.07562484757494 |
| 14.54 | 41.17 | 1015.15 | 67.78 | 470.19 | 468.4620083288529 |
| 14.54 | 43.14 | 1010.26 | 82.98 | 465.45 | 465.35539799683 |
| 14.55 | 44.84 | 1023.83 | 87.6 | 465.14 | 465.34301144661265 |
| 14.57 | 41.79 | 1007.61 | 82.85 | 457.21 | 465.4373435562456 |
| 14.58 | 41.92 | 1030.42 | 61.96 | 462.69 | 470.2899851111997 |
| 14.58 | 42.07 | 1017.9 | 86.14 | 460.66 | 465.705988879045 |
| 14.64 | 44.92 | 1025.54 | 91.12 | 462.64 | 464.775180629532 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 | 471.7241650123044 |
| 14.65 | 35.4 | 1016.16 | 60.26 | 469.61 | 470.86762962520095 |
| 14.65 | 41.92 | 1030.61 | 63.07 | 464.95 | 470.0085068177468 |
| 14.65 | 44.84 | 1023.39 | 87.76 | 467.18 | 465.090966572103 |
| 14.66 | 41.76 | 1022.12 | 78.13 | 463.6 | 467.14100725665213 |
| 14.66 | 42.07 | 1018.14 | 84.68 | 462.77 | 465.7842034756254 |
| 14.72 | 39.0 | 1016.42 | 76.42 | 464.93 | 467.49881987016624 |
| 14.74 | 43.71 | 1024.35 | 81.53 | 465.49 | 466.18608052784475 |
| 14.76 | 39.64 | 1010.37 | 81.99 | 465.82 | 465.9570356485115 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 | 465.6600911572598 |
| 14.77 | 58.2 | 1018.78 | 83.83 | 460.54 | 461.72665249570616 |
| 14.78 | 38.58 | 1017.02 | 82.4 | 460.54 | 466.6642858393167 |
| 14.79 | 47.83 | 1007.27 | 92.04 | 463.22 | 462.1388114830899 |
| 14.81 | 39.58 | 1011.62 | 73.64 | 467.32 | 467.1954080636746 |
| 14.81 | 40.71 | 1018.54 | 73.0 | 467.66 | 467.57038570428426 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 | 466.8779893764293 |
| 14.82 | 42.74 | 1028.04 | 69.81 | 466.34 | 468.28371557585353 |
| 14.83 | 53.82 | 1016.57 | 63.47 | 464.0 | 465.49312878230023 |
| 14.84 | 71.14 | 1019.61 | 66.78 | 454.16 | 460.9202947940051 |
| 14.85 | 45.01 | 1013.12 | 85.53 | 460.19 | 464.1520666190232 |
| 14.86 | 37.85 | 1010.58 | 86.8 | 467.71 | 465.5258417359535 |
| 14.87 | 41.23 | 997.39 | 82.06 | 465.01 | 464.28156360850716 |
| 14.87 | 54.3 | 1017.17 | 71.57 | 462.87 | 464.1635216732549 |
| 14.88 | 42.28 | 1007.26 | 71.3 | 466.73 | 466.3736543730528 |
| 14.91 | 39.28 | 1014.57 | 75.23 | 469.34 | 467.08552274770574 |
| 14.91 | 40.73 | 1017.44 | 86.91 | 458.99 | 465.25377872135545 |
| 14.92 | 41.16 | 1004.83 | 83.7 | 465.72 | 464.56900508273293 |
| 14.92 | 46.18 | 1014.21 | 98.82 | 465.63 | 461.87533959682145 |
| 14.93 | 42.44 | 1012.65 | 86.8 | 471.24 | 464.4149735638803 |
| 14.94 | 40.0 | 1017.69 | 65.43 | 456.41 | 468.5317634787664 |
| 14.94 | 42.77 | 1018.06 | 75.35 | 460.51 | 466.42415027580006 |
| 14.98 | 39.58 | 1011.78 | 75.07 | 467.77 | 466.6719213555882 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 | 464.60786745115865 |
| 15.01 | 39.52 | 1017.53 | 72.0 | 468.02 | 467.544960954765 |
| 15.02 | 37.64 | 1016.49 | 83.28 | 463.93 | 466.26419991369926 |
| 15.03 | 43.71 | 1025.07 | 83.25 | 463.12 | 465.43441550001705 |
| 15.03 | 44.45 | 1020.94 | 65.57 | 460.06 | 467.4928608009505 |
| 15.08 | 42.77 | 1018.67 | 73.89 | 461.6 | 466.41675498345813 |
| 15.09 | 41.76 | 1022.4 | 76.22 | 463.27 | 466.61302775183003 |
| 15.11 | 43.67 | 1012.06 | 91.75 | 467.82 | 462.99098636012917 |
| 15.12 | 48.92 | 1011.8 | 72.93 | 462.59 | 464.3870766666383 |
| 15.14 | 39.72 | 1002.96 | 72.52 | 460.15 | 465.9823776729848 |
| 15.14 | 44.21 | 1019.97 | 83.86 | 463.1 | 464.5934173732165 |
| 15.15 | 53.82 | 1016.34 | 62.59 | 461.6 | 464.9855482511964 |
| 15.19 | 42.03 | 1017.38 | 71.66 | 462.64 | 466.6093718548404 |
| 15.21 | 50.88 | 1014.24 | 100.11 | 460.56 | 459.9584434481331 |
| 15.24 | 48.6 | 1007.08 | 86.49 | 459.85 | 461.87302187524506 |
| 15.27 | 38.73 | 1002.83 | 77.77 | 465.99 | 465.2019993258076 |
| 15.27 | 39.54 | 1010.64 | 81.91 | 464.49 | 465.03190605144545 |
| 15.29 | 38.73 | 1000.9 | 81.17 | 468.62 | 464.51031407803373 |
| 15.31 | 40.66 | 1016.46 | 84.64 | 458.26 | 464.7510595901315 |
| 15.31 | 41.35 | 1005.09 | 95.25 | 466.5 | 462.10563966655616 |
| 15.32 | 45.01 | 1013.3 | 83.72 | 459.31 | 463.52420449023833 |
| 15.34 | 43.5 | 1021.18 | 79.44 | 459.77 | 465.12795484714036 |
| 15.34 | 71.14 | 1019.79 | 77.56 | 457.1 | 458.39794118753656 |
| 15.4 | 38.73 | 1000.67 | 79.71 | 469.18 | 464.49240158564226 |
| 15.41 | 42.44 | 1012.6 | 86.74 | 472.28 | 463.4938095964465 |
| 15.46 | 42.07 | 1017.9 | 81.12 | 459.15 | 464.74092024201923 |
| 15.47 | 43.13 | 1015.11 | 50.5 | 466.63 | 468.69706628494185 |
| 15.48 | 53.82 | 1016.1 | 64.77 | 462.69 | 464.01147313398934 |
| 15.5 | 44.34 | 1019.21 | 65.21 | 468.53 | 466.52540885533836 |
| 15.5 | 49.25 | 1021.41 | 77.92 | 453.99 | 463.6262300043777 |
| 15.52 | 41.93 | 1022.97 | 52.92 | 471.97 | 469.18664060318713 |
| 15.52 | 58.59 | 1014.12 | 91.22 | 457.74 | 458.7253721171194 |
| 15.55 | 39.63 | 1004.98 | 89.82 | 466.83 | 462.85471321992253 |
| 15.55 | 43.02 | 1011.97 | 44.66 | 466.2 | 469.16650047432273 |
| 15.55 | 43.71 | 1024.34 | 79.61 | 465.14 | 464.90298984127037 |
| 15.55 | 45.09 | 1014.33 | 62.19 | 457.36 | 466.2852656032666 |
| 15.61 | 38.52 | 1018.4 | 80.99 | 439.21 | 465.39633546049805 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 | 462.31339644999167 |
| 15.62 | 58.59 | 1013.91 | 97.6 | 457.3 | 457.58467899039783 |
| 15.66 | 41.35 | 1001.68 | 86.26 | 463.57 | 462.46440155189674 |
| 15.67 | 45.17 | 1018.73 | 94.74 | 462.09 | 461.6436673101792 |
| 15.69 | 37.87 | 1021.18 | 84.38 | 465.41 | 465.13586499514474 |
| 15.69 | 39.3 | 1019.03 | 60.57 | 464.17 | 468.0777122780922 |
| 15.7 | 42.99 | 1007.51 | 76.05 | 464.59 | 463.94240853336476 |
| 15.75 | 36.99 | 1007.67 | 93.8 | 464.38 | 462.7655254273002 |
| 15.75 | 39.99 | 1007.02 | 77.44 | 464.95 | 464.3512532840681 |
| 15.79 | 58.86 | 1015.85 | 91.91 | 455.15 | 458.1774466179169 |
| 15.81 | 58.59 | 1014.41 | 90.03 | 456.91 | 458.36321179088014 |
| 15.84 | 41.04 | 1025.64 | 63.43 | 456.21 | 467.4754642409999 |
| 15.85 | 42.28 | 1007.4 | 82.12 | 467.3 | 462.93565136830296 |
| 15.85 | 49.69 | 1015.48 | 88.65 | 464.72 | 460.79339608207107 |
| 15.86 | 38.62 | 1016.65 | 67.51 | 462.58 | 466.71318747004864 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 | 469.2173130099923 |
| 15.86 | 43.5 | 1021.22 | 75.38 | 459.42 | 464.720482732063 |
| 15.92 | 37.64 | 1014.93 | 83.73 | 464.14 | 464.335595847906 |
| 15.92 | 39.16 | 1006.59 | 71.32 | 460.45 | 465.0880608446689 |
| 15.92 | 41.2 | 1016.04 | 73.37 | 468.91 | 465.0497057218854 |
| 15.96 | 41.66 | 1011.93 | 55.47 | 466.39 | 467.13452750857033 |
| 15.98 | 39.64 | 1009.31 | 81.21 | 467.22 | 463.63133669846115 |
| 15.98 | 44.68 | 1018.48 | 85.94 | 462.77 | 462.43127985662505 |
| 15.99 | 39.63 | 1006.09 | 89.92 | 464.95 | 462.0817954663473 |
| 16.0 | 40.66 | 1016.12 | 89.23 | 457.12 | 462.7228887148107 |
| 16.0 | 44.9 | 1020.5 | 80.89 | 461.5 | 463.2389898882613 |
| 16.0 | 45.09 | 1014.31 | 60.02 | 458.46 | 465.7322155460913 |
| 16.01 | 43.69 | 1017.12 | 62.43 | 465.89 | 465.9391561803266 |
| 16.02 | 39.54 | 1007.73 | 72.81 | 466.15 | 464.67588000342283 |
| 16.02 | 44.9 | 1009.3 | 82.62 | 455.48 | 462.03627301808893 |
| 16.09 | 44.71 | 1017.86 | 42.74 | 464.95 | 468.46315966006046 |
| 16.09 | 65.46 | 1013.84 | 85.52 | 454.88 | 456.7218449478403 |
| 16.12 | 45.87 | 1008.15 | 86.12 | 457.41 | 460.9973530979208 |
| 16.14 | 44.71 | 1014.83 | 39.41 | 468.88 | 468.60583110746245 |
| 16.16 | 25.88 | 1009.58 | 72.24 | 461.86 | 468.0452621538944 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 | 461.16748971043216 |
| 16.19 | 36.99 | 1007.37 | 92.4 | 462.94 | 462.09664222758136 |
| 16.2 | 45.76 | 1014.73 | 89.84 | 460.87 | 460.8634611992421 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 | 464.39198726436115 |
| 16.22 | 50.88 | 1014.33 | 100.09 | 454.94 | 458.02055270119473 |
| 16.23 | 43.69 | 1016.4 | 68.9 | 466.22 | 464.5123542802937 |
| 16.29 | 53.82 | 1014.97 | 73.15 | 459.95 | 461.1346442030387 |
| 16.3 | 41.16 | 1007.88 | 76.61 | 463.47 | 463.18977819659995 |
| 16.31 | 52.75 | 1024.4 | 55.69 | 456.58 | 464.6775725663142 |
| 16.32 | 43.56 | 1014.4 | 59.77 | 463.57 | 465.5402356742791 |
| 16.33 | 42.44 | 1013.98 | 84.9 | 462.44 | 462.1000323229214 |
| 16.34 | 47.45 | 1009.41 | 92.96 | 448.59 | 459.28384302135794 |
| 16.36 | 39.99 | 1008.91 | 72.48 | 462.5 | 464.0520812837949 |
| 16.37 | 36.99 | 1006.37 | 90.11 | 463.76 | 462.0021066209579 |
| 16.39 | 52.75 | 1024.42 | 54.61 | 459.48 | 464.6824431291315 |
| 16.39 | 58.59 | 1014.58 | 90.34 | 455.05 | 457.21309738475657 |
| 16.4 | 44.9 | 1009.22 | 82.31 | 456.11 | 461.3420214113817 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 | 467.91529134370023 |
| 16.47 | 38.01 | 1022.3 | 72.29 | 461.54 | 465.4513233379416 |
| 16.47 | 44.89 | 1010.04 | 82.01 | 459.69 | 461.3200136826322 |
| 16.49 | 49.39 | 1018.1 | 93.13 | 461.54 | 459.19347653551756 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 | 459.1522349051031 |
| 16.51 | 51.86 | 1022.37 | 81.18 | 442.48 | 460.6299621913311 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 | 465.94867946942674 |
| 16.56 | 42.99 | 1007.48 | 74.45 | 464.62 | 462.5145658398506 |
| 16.57 | 43.7 | 1015.67 | 71.95 | 465.78 | 463.3496922983309 |
| 16.57 | 53.82 | 1015.17 | 63.69 | 462.52 | 461.99087118644087 |
| 16.57 | 63.31 | 1016.19 | 81.02 | 454.78 | 457.1797966088055 |
| 16.59 | 43.56 | 1012.88 | 59.61 | 465.03 | 464.9190479188059 |
| 16.62 | 39.99 | 1007.07 | 77.14 | 463.74 | 462.72099107520137 |
| 16.62 | 54.3 | 1017.99 | 63.76 | 459.59 | 461.9941154580848 |
| 16.65 | 46.18 | 1010.6 | 95.69 | 465.6 | 458.7011562788462 |
| 16.7 | 36.99 | 1006.19 | 89.33 | 464.7 | 461.4647200415145 |
| 16.73 | 54.3 | 1017.96 | 59.44 | 460.54 | 462.40970062782714 |
| 16.77 | 42.28 | 1007.53 | 73.19 | 465.52 | 462.47440172010425 |
| 16.82 | 41.66 | 1010.49 | 63.14 | 465.64 | 464.23959443772316 |
| 16.82 | 45.0 | 1022.05 | 37.28 | 468.22 | 468.12040219816635 |
| 16.85 | 39.64 | 1008.82 | 80.81 | 464.2 | 461.97170222353844 |
| 16.85 | 42.24 | 1017.43 | 66.01 | 472.63 | 464.18342103476823 |
| 16.87 | 52.05 | 1012.7 | 71.63 | 460.31 | 460.4941449517029 |
| 16.89 | 49.21 | 1015.19 | 70.39 | 458.25 | 461.5472235985969 |
| 16.94 | 49.64 | 1024.43 | 69.22 | 459.25 | 462.26646301676067 |
| 16.97 | 42.86 | 1013.92 | 74.8 | 463.62 | 462.2293585659873 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 | 464.22591401634116 |
| 17.02 | 51.86 | 1021.53 | 81.28 | 460.0 | 459.5632798612247 |
| 17.03 | 43.99 | 1021.5 | 82.32 | 460.25 | 461.3519558174483 |
| 17.07 | 41.35 | 1005.88 | 83.43 | 464.6 | 460.4994805562497 |
| 17.08 | 38.58 | 1015.41 | 73.42 | 461.49 | 463.40687222781077 |
| 17.08 | 58.86 | 1016.04 | 87.46 | 449.98 | 456.35386691539543 |
| 17.19 | 43.14 | 1014.34 | 68.62 | 464.72 | 462.67093183857946 |
| 17.27 | 43.52 | 1021.37 | 76.73 | 460.08 | 461.81109623075827 |
| 17.27 | 44.9 | 1007.85 | 78.8 | 454.19 | 460.0644340540906 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 | 462.014274652855 |
| 17.3 | 43.72 | 1009.64 | 77.86 | 456.55 | 460.5836092644097 |
| 17.32 | 43.7 | 1015.13 | 61.66 | 464.5 | 463.360199769915 |
| 17.32 | 44.34 | 1019.52 | 56.24 | 468.8 | 464.34868588625807 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 | 461.6467454037935 |
| 17.35 | 52.72 | 1026.31 | 58.01 | 463.65 | 462.4960995942487 |
| 17.36 | 43.96 | 1013.02 | 79.59 | 466.36 | 460.43082906265056 |
| 17.37 | 48.92 | 1011.91 | 58.4 | 455.53 | 462.1757595182412 |
| 17.39 | 46.21 | 1013.94 | 82.73 | 454.06 | 459.4288341311474 |
| 17.4 | 63.09 | 1020.84 | 92.58 | 453.0 | 454.3258801823864 |
| 17.41 | 40.55 | 1003.91 | 76.87 | 461.47 | 460.83972369537236 |
| 17.44 | 44.89 | 1009.9 | 80.54 | 457.67 | 459.652078633235 |
| 17.45 | 50.9 | 1012.16 | 83.8 | 458.67 | 457.84281119185783 |
| 17.46 | 39.99 | 1008.52 | 69.73 | 461.01 | 462.29977029786545 |
| 17.48 | 52.9 | 1020.03 | 76.47 | 458.34 | 458.99629100134234 |
| 17.61 | 56.53 | 1019.5 | 82.3 | 457.01 | 456.94689658887796 |
| 17.64 | 57.76 | 1016.28 | 85.04 | 455.75 | 455.92052796835753 |
| 17.66 | 60.08 | 1017.22 | 86.96 | 456.62 | 455.0999707958263 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 | 463.6885973525347 |
| 17.67 | 50.88 | 1015.64 | 90.55 | 456.16 | 456.72206228080466 |
| 17.7 | 49.21 | 1015.16 | 67.91 | 455.97 | 460.34419741616995 |
| 17.74 | 49.69 | 1006.09 | 80.7 | 457.05 | 457.5432003190509 |
| 17.74 | 50.88 | 1015.56 | 89.78 | 457.71 | 456.69285780084897 |
| 17.75 | 55.5 | 1020.15 | 81.26 | 459.43 | 457.13828420727975 |
| 17.77 | 52.9 | 1020.11 | 81.51 | 457.98 | 457.7082041695146 |
| 17.79 | 40.12 | 1012.74 | 79.03 | 459.13 | 460.61769921749 |
| 17.82 | 49.15 | 1020.73 | 70.25 | 457.35 | 460.2397779497155 |
| 17.83 | 66.86 | 1011.65 | 77.31 | 456.56 | 454.03599822567077 |
| 17.84 | 61.27 | 1020.1 | 70.68 | 454.57 | 457.06546864918414 |
| 17.85 | 48.14 | 1017.16 | 86.68 | 451.9 | 457.7462919446442 |
| 17.86 | 50.88 | 1015.59 | 88.28 | 457.33 | 456.68265807481106 |
| 17.89 | 42.42 | 1008.92 | 65.08 | 467.59 | 461.5754309315541 |
| 17.89 | 44.6 | 1014.48 | 42.51 | 463.99 | 464.77705443177615 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 | 459.8014970847607 |
| 17.9 | 58.33 | 1013.6 | 85.81 | 452.28 | 454.94641693932414 |
| 17.94 | 62.1 | 1019.81 | 82.65 | 453.55 | 454.8958623057626 |
| 17.97 | 65.94 | 1012.92 | 88.22 | 448.88 | 452.5071708494883 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 | 455.24182428555105 |
| 18.0 | 44.06 | 1016.8 | 78.88 | 454.59 | 459.58273190303845 |
| 18.01 | 62.26 | 1011.89 | 89.29 | 451.14 | 453.10756062981807 |
| 18.02 | 53.16 | 1013.41 | 82.84 | 458.01 | 456.42171751548034 |
| 18.03 | 53.16 | 1013.02 | 81.95 | 456.55 | 456.5005129659639 |
| 18.03 | 53.16 | 1013.06 | 82.03 | 458.04 | 456.4920988999565 |
| 18.04 | 44.85 | 1015.13 | 48.4 | 463.31 | 463.61908166044077 |
| 18.06 | 65.48 | 1018.79 | 77.95 | 454.34 | 454.4243094498896 |
| 18.11 | 58.95 | 1016.61 | 89.17 | 454.29 | 454.14166454199494 |
| 18.13 | 60.1 | 1009.67 | 84.75 | 455.82 | 453.8961915229473 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 | 453.6649941349345 |
| 18.14 | 67.71 | 1003.82 | 95.69 | 442.45 | 449.9074433355401 |
| 18.16 | 43.72 | 1008.64 | 75.22 | 454.98 | 459.22851589459475 |
| 18.16 | 43.96 | 1012.78 | 78.33 | 465.26 | 459.05202239898534 |
| 18.17 | 52.08 | 1001.91 | 100.09 | 451.06 | 452.94903640134396 |
| 18.17 | 66.86 | 1011.29 | 78.48 | 452.77 | 453.1802042498492 |
| 18.2 | 52.72 | 1025.87 | 54.26 | 463.47 | 461.3678096135659 |
| 18.21 | 39.54 | 1009.81 | 70.92 | 461.73 | 460.89674705500823 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 | 463.8188758742396 |
| 18.22 | 45.09 | 1013.62 | 75.56 | 454.74 | 459.1270333631719 |
| 18.22 | 58.2 | 1017.09 | 81.92 | 451.04 | 455.213182837326 |
| 18.24 | 49.5 | 1014.37 | 79.36 | 464.13 | 457.4956830813669 |
| 18.25 | 60.1 | 1009.72 | 90.15 | 456.25 | 452.8810496638165 |
| 18.26 | 58.96 | 1014.04 | 69.7 | 457.43 | 456.48090497858726 |
| 18.27 | 58.2 | 1018.34 | 72.73 | 448.17 | 456.5591352486571 |
| 18.28 | 60.1 | 1009.72 | 85.79 | 452.93 | 453.45921998591575 |
| 18.31 | 62.1 | 1020.38 | 79.02 | 455.24 | 454.7581350474279 |
| 18.32 | 39.53 | 1008.15 | 64.85 | 454.44 | 461.43742015467615 |
| 18.32 | 42.28 | 1007.86 | 45.62 | 460.27 | 463.53345887643894 |
| 18.32 | 65.94 | 1012.74 | 86.77 | 450.5 | 452.02894677235804 |
| 18.34 | 44.63 | 1000.76 | 89.27 | 455.22 | 455.963340998892 |
| 18.34 | 50.59 | 1018.42 | 83.95 | 457.17 | 456.69115708185933 |
| 18.36 | 53.16 | 1013.31 | 83.18 | 458.47 | 455.70816977608035 |
| 18.36 | 56.65 | 1020.29 | 82.0 | 456.49 | 455.5784197907349 |
| 18.38 | 55.28 | 1020.22 | 68.33 | 451.29 | 457.8698842565306 |
| 18.4 | 50.16 | 1011.51 | 98.07 | 453.78 | 454.0602820488982 |
| 18.41 | 42.44 | 1012.66 | 65.93 | 463.2 | 460.7479119618785 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 | 453.92151217696727 |
| 18.42 | 60.1 | 1009.77 | 86.75 | 451.93 | 453.0532072948905 |
| 18.42 | 63.94 | 1020.47 | 74.47 | 450.55 | 454.758298968509 |
| 18.48 | 46.48 | 1007.53 | 82.57 | 461.49 | 456.7605922899199 |
| 18.48 | 58.59 | 1015.61 | 85.14 | 452.52 | 454.0242328275001 |
| 18.5 | 52.08 | 1006.23 | 100.09 | 451.23 | 452.6641989591518 |
| 18.51 | 48.06 | 1015.14 | 79.83 | 455.44 | 457.32803096446935 |
| 18.53 | 63.91 | 1010.26 | 97.8 | 440.64 | 450.3190565318654 |
| 18.55 | 41.85 | 1015.24 | 62.47 | 467.51 | 461.3397464375879 |
| 18.55 | 46.48 | 1007.34 | 80.67 | 452.37 | 456.8872770659861 |
| 18.56 | 42.28 | 1007.75 | 60.89 | 462.05 | 460.8339970164602 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 | 462.10615685280686 |
| 18.63 | 45.87 | 1007.98 | 79.9 | 453.79 | 457.0494808979769 |
| 18.65 | 52.08 | 1005.48 | 99.94 | 449.55 | 452.3356980438659 |
| 18.66 | 56.53 | 1020.13 | 80.04 | 459.45 | 455.30258286482143 |
| 18.68 | 43.69 | 1016.68 | 48.88 | 463.02 | 462.7299869900826 |
| 18.68 | 56.65 | 1020.38 | 80.26 | 455.79 | 455.2223463598715 |
| 18.68 | 62.1 | 1019.78 | 83.67 | 453.25 | 453.31727625144936 |
| 18.73 | 40.12 | 1013.19 | 74.12 | 454.71 | 459.55748654713716 |
| 18.73 | 46.48 | 1007.19 | 79.23 | 450.74 | 456.7379403460756 |
| 18.8 | 47.83 | 1005.86 | 76.77 | 453.9 | 456.5169354267787 |
| 18.81 | 52.08 | 1004.43 | 99.6 | 450.87 | 451.9912034594031 |
| 18.83 | 48.98 | 1016.33 | 74.23 | 453.28 | 457.39523074964615 |
| 18.84 | 61.27 | 1019.64 | 71.95 | 454.47 | 454.91390716792046 |
| 18.86 | 50.78 | 1008.46 | 91.67 | 446.7 | 453.70377279073705 |
| 18.87 | 43.43 | 1009.16 | 69.5 | 454.58 | 458.80810089986693 |
| 18.87 | 52.05 | 1012.02 | 53.46 | 458.64 | 459.2317295422404 |
| 18.89 | 66.86 | 1012.38 | 71.96 | 454.02 | 452.8313052555021 |
| 18.9 | 62.96 | 1020.69 | 80.57 | 455.88 | 453.2048261109298 |
| 18.91 | 43.14 | 1013.56 | 58.34 | 460.19 | 460.78946144208953 |
| 18.95 | 46.21 | 1013.47 | 81.22 | 457.58 | 456.60185019595883 |
| 18.97 | 50.59 | 1016.01 | 74.9 | 459.68 | 456.60000256329795 |
| 18.98 | 38.52 | 1018.85 | 63.16 | 454.6 | 461.5337919917975 |
| 18.99 | 56.65 | 1020.46 | 77.16 | 457.55 | 455.08314377928764 |
| 19.02 | 50.66 | 1013.04 | 85.25 | 455.42 | 454.7344713102365 |
| 19.05 | 53.16 | 1013.22 | 82.8 | 455.76 | 454.4253732327452 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 | 450.84382297953874 |
| 19.06 | 56.65 | 1020.82 | 82.14 | 455.7 | 454.2509501800389 |
| 19.08 | 44.63 | 1020.05 | 41.07 | 455.95 | 463.1377560912732 |
| 19.08 | 58.59 | 1013.42 | 68.88 | 451.05 | 455.0606464477159 |
| 19.11 | 58.95 | 1017.07 | 85.49 | 449.89 | 452.78710305999687 |
| 19.12 | 50.16 | 1011.52 | 99.71 | 451.49 | 452.43308379405045 |
| 19.13 | 42.18 | 1001.45 | 98.77 | 456.04 | 453.7206916287245 |
| 19.14 | 56.65 | 1020.84 | 82.97 | 458.06 | 453.97719041615505 |
| 19.2 | 58.71 | 1009.8 | 84.62 | 448.17 | 452.20842313451385 |
| 19.22 | 62.1 | 1019.43 | 79.19 | 451.8 | 452.90074763749135 |
| 19.23 | 41.67 | 1012.53 | 48.86 | 465.66 | 461.8378158906793 |
| 19.24 | 58.33 | 1013.65 | 85.47 | 449.26 | 452.41543202652065 |
| 19.25 | 43.43 | 1012.01 | 73.26 | 451.08 | 457.75864371455697 |
| 19.26 | 44.34 | 1019.45 | 51.32 | 467.72 | 461.3187533462041 |
| 19.31 | 43.56 | 1013.65 | 41.54 | 463.35 | 462.37131638295244 |
| 19.31 | 60.07 | 1014.86 | 69.37 | 453.47 | 454.29376940501464 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 | 453.15381920341724 |
| 19.43 | 52.9 | 1018.35 | 61.12 | 456.08 | 457.3375291656138 |
| 19.44 | 59.21 | 1018.5 | 88.35 | 448.04 | 451.78495851322157 |
| 19.47 | 58.79 | 1016.8 | 87.26 | 450.17 | 451.8524216632198 |
| 19.54 | 44.57 | 1007.19 | 78.93 | 450.54 | 455.69553323527913 |
| 19.54 | 50.66 | 1014.7 | 84.53 | 456.61 | 453.9716415466405 |
| 19.57 | 68.61 | 1011.13 | 96.4 | 448.73 | 447.41632457686086 |
| 19.6 | 48.14 | 1013.18 | 68.71 | 456.57 | 456.66826631159057 |
| 19.6 | 60.95 | 1015.4 | 84.26 | 456.06 | 451.3868160236597 |
| 19.61 | 56.65 | 1020.64 | 63.74 | 457.41 | 455.8596185860852 |
| 19.62 | 58.79 | 1017.59 | 87.39 | 446.29 | 451.60844251280884 |
| 19.62 | 68.63 | 1012.26 | 68.05 | 453.84 | 451.542577765937 |
| 19.64 | 56.65 | 1020.79 | 73.88 | 456.03 | 454.3347436794228 |
| 19.65 | 42.23 | 1013.04 | 75.9 | 461.16 | 456.98501253896984 |
| 19.65 | 57.85 | 1011.73 | 93.89 | 450.5 | 450.35966629930124 |
| 19.66 | 51.43 | 1010.17 | 84.19 | 459.12 | 453.2290277149437 |
| 19.67 | 60.77 | 1017.33 | 88.13 | 444.87 | 450.8892362675085 |
| 19.68 | 44.34 | 1019.49 | 49.5 | 468.27 | 460.77739524450277 |
| 19.68 | 51.19 | 1008.64 | 94.88 | 452.04 | 451.5662781980391 |
| 19.68 | 56.65 | 1020.75 | 67.25 | 456.89 | 455.22151625901614 |
| 19.69 | 39.72 | 1001.49 | 60.34 | 456.55 | 458.86327155628703 |
| 19.69 | 56.65 | 1020.84 | 72.14 | 455.07 | 454.4962025118092 |
| 19.7 | 51.3 | 1014.88 | 88.1 | 454.94 | 452.9973261711798 |
| 19.7 | 52.84 | 1004.86 | 89.72 | 444.64 | 451.56134671760844 |
| 19.72 | 44.78 | 1009.27 | 39.18 | 464.54 | 461.26403285215525 |
| 19.73 | 49.69 | 1007.78 | 73.02 | 454.28 | 454.96273138933435 |
| 19.73 | 68.63 | 1012.41 | 74.12 | 450.26 | 450.45712572408434 |
| 19.76 | 62.1 | 1020.07 | 73.55 | 450.44 | 452.7340333117575 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 | 451.3669335966618 |
| 19.79 | 60.1 | 1010.47 | 84.04 | 452.41 | 450.86300710254875 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 | 450.87541624186673 |
| 19.8 | 58.79 | 1017.04 | 87.71 | 449.66 | 451.1697942873412 |
| 19.8 | 67.71 | 1005.58 | 69.65 | 446.03 | 450.6475445784032 |
| 19.82 | 46.63 | 1013.17 | 87.1 | 456.36 | 453.93684539976664 |
| 19.83 | 46.33 | 1013.27 | 96.4 | 451.22 | 452.6438110022995 |
| 19.83 | 59.21 | 1012.67 | 96.42 | 440.03 | 449.38085095551605 |
| 19.86 | 41.67 | 1012.31 | 53.9 | 462.86 | 459.86949886435633 |
| 19.87 | 48.14 | 1016.94 | 81.56 | 451.14 | 454.5790164502554 |
| 19.87 | 49.69 | 1012.23 | 68.57 | 456.03 | 455.7041227162542 |
| 19.89 | 50.78 | 1008.85 | 92.97 | 446.35 | 451.5591661954991 |
| 19.89 | 51.43 | 1007.38 | 91.79 | 448.85 | 451.4495789708702 |
| 19.89 | 68.08 | 1012.65 | 80.25 | 448.71 | 449.41092940189 |
| 19.92 | 46.97 | 1014.32 | 69.17 | 459.39 | 456.368436088565 |
| 19.93 | 56.65 | 1020.7 | 62.82 | 456.53 | 455.3814815227917 |
| 19.94 | 44.63 | 1004.73 | 78.48 | 455.58 | 454.77441817350825 |
| 19.94 | 44.9 | 1008.52 | 74.69 | 459.47 | 455.56852272126105 |
| 19.94 | 56.53 | 1020.48 | 76.43 | 453.8 | 453.3887778929569 |
| 19.95 | 58.46 | 1017.45 | 89.46 | 447.1 | 450.7408294300028 |
| 19.97 | 50.78 | 1008.75 | 92.7 | 446.57 | 451.436105216529 |
| 19.99 | 40.79 | 1003.15 | 87.55 | 455.28 | 454.1835978057384 |
| 20.01 | 45.09 | 1014.21 | 38.19 | 453.96 | 461.1739549532308 |
| 20.01 | 68.63 | 1012.34 | 69.49 | 454.5 | 450.58677338372456 |
| 20.03 | 60.77 | 1017.23 | 87.82 | 449.31 | 450.2319334353776 |
| 20.04 | 49.39 | 1020.62 | 78.84 | 449.63 | 454.6358406173096 |
| 20.04 | 58.2 | 1017.56 | 74.31 | 448.92 | 452.85108866192866 |
| 20.08 | 54.42 | 1011.79 | 89.35 | 457.14 | 451.05259673451775 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 | 451.5232844413466 |
| 20.1 | 57.17 | 1011.96 | 87.68 | 452.67 | 450.58585768893414 |
| 20.11 | 51.19 | 1007.82 | 92.06 | 449.03 | 451.0815006280822 |
| 20.12 | 58.12 | 1015.47 | 79.38 | 453.33 | 451.80697350389795 |
| 20.16 | 57.76 | 1019.34 | 72.1 | 455.13 | 453.19662660647845 |
| 20.19 | 44.57 | 1009.2 | 72.13 | 454.36 | 455.59739505823774 |
| 20.19 | 66.86 | 1012.97 | 64.7 | 454.84 | 451.430922332517 |
| 20.21 | 54.9 | 1016.82 | 66.56 | 454.23 | 454.4162557726164 |
| 20.21 | 69.94 | 1009.33 | 83.96 | 447.06 | 447.51848167260005 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 | 457.4899833309971 |
| 20.24 | 56.65 | 1020.72 | 62.9 | 455.49 | 454.7734968589603 |
| 20.25 | 44.78 | 1007.93 | 40.16 | 462.44 | 459.9896954813451 |
| 20.28 | 48.78 | 1017.4 | 82.51 | 451.59 | 453.5274885789799 |
| 20.28 | 62.52 | 1017.89 | 75.67 | 452.45 | 451.13958592197287 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 | 451.01129177115814 |
| 20.33 | 57.76 | 1016.47 | 75.35 | 450.25 | 452.16097295111786 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 | 455.0355456385039 |
| 20.43 | 63.09 | 1016.46 | 91.78 | 445.58 | 448.2416124360999 |
| 20.45 | 59.8 | 1015.13 | 79.21 | 452.96 | 450.748723139911 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 | 453.9480760674323 |
| 20.51 | 39.72 | 1002.25 | 47.97 | 452.39 | 459.1480198621134 |
| 20.51 | 68.08 | 1012.73 | 78.05 | 445.96 | 448.54249260358677 |
| 20.56 | 60.08 | 1017.79 | 78.08 | 452.8 | 450.84813038147 |
| 20.56 | 64.45 | 1012.24 | 53.09 | 458.97 | 452.9523382793844 |
| 20.58 | 39.53 | 1005.68 | 62.09 | 460.1 | 457.2797775931854 |
| 20.59 | 59.8 | 1015.27 | 77.94 | 453.83 | 450.67534900317014 |
| 20.6 | 59.15 | 1013.32 | 91.07 | 443.76 | 448.7439698617689 |
| 20.61 | 60.1 | 1010.84 | 80.57 | 450.46 | 449.81767655065505 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 | 449.46273191454395 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 | 450.23276134284185 |
| 20.61 | 65.61 | 1014.91 | 83.82 | 449.72 | 448.3011628860887 |
| 20.64 | 61.86 | 1012.81 | 99.97 | 447.14 | 446.65132022669536 |
| 20.65 | 41.67 | 1012.76 | 45.27 | 455.5 | 459.6412858596406 |
| 20.65 | 57.5 | 1016.04 | 87.45 | 448.22 | 449.8084139802319 |
| 20.68 | 63.86 | 1015.73 | 74.36 | 447.84 | 450.0492245621943 |
| 20.69 | 50.78 | 1008.71 | 91.95 | 447.58 | 450.1534891767275 |
| 20.71 | 58.18 | 1007.63 | 98.44 | 447.06 | 447.2352893702614 |
| 20.72 | 63.94 | 1017.17 | 59.83 | 447.69 | 452.1889854808296 |
| 20.76 | 44.58 | 1017.09 | 57.47 | 462.01 | 457.2763644769643 |
| 20.76 | 59.04 | 1012.51 | 85.39 | 448.92 | 449.22543586447597 |
| 20.76 | 69.05 | 1001.89 | 77.86 | 442.82 | 446.9636998742384 |
| 20.77 | 43.77 | 1010.76 | 63.12 | 453.46 | 456.1194901357003 |
| 20.78 | 58.86 | 1016.02 | 77.29 | 446.2 | 450.6991034809647 |
| 20.8 | 69.45 | 1013.7 | 82.48 | 443.77 | 447.0742816761689 |
| 20.82 | 63.77 | 1014.28 | 86.37 | 448.9 | 447.93156732156075 |
| 20.83 | 44.78 | 1008.51 | 35.9 | 460.6 | 459.53962876300176 |
| 20.85 | 59.21 | 1012.9 | 75.97 | 444.44 | 450.41539217977487 |
| 20.87 | 57.19 | 1006.5 | 77.0 | 445.95 | 450.2091683575852 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 | 450.5270199129538 |
| 20.9 | 67.07 | 1005.43 | 82.85 | 443.46 | 446.7475520504839 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 | 444.5071996193093 |
| 20.94 | 44.78 | 1008.14 | 35.7 | 465.57 | 459.3265106832487 |
| 20.94 | 68.12 | 1012.43 | 78.2 | 446.41 | 447.6568115900025 |
| 20.95 | 44.89 | 1010.48 | 67.97 | 450.39 | 454.7627521673882 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 | 454.2185133216816 |
| 20.95 | 70.72 | 1009.96 | 87.73 | 445.66 | 445.39798853968557 |
| 20.96 | 69.48 | 1011.04 | 82.63 | 444.31 | 446.5197598700026 |
| 20.98 | 60.1 | 1011.07 | 79.44 | 450.95 | 449.2875712413884 |
| 20.99 | 67.07 | 1005.17 | 82.41 | 442.02 | 446.61697690688305 |
| 21.01 | 58.96 | 1014.33 | 61.8 | 453.88 | 452.35263519535 |
| 21.06 | 50.59 | 1016.42 | 66.12 | 454.15 | 453.8829146493028 |
| 21.06 | 62.91 | 1011.92 | 75.52 | 455.22 | 449.0737291884585 |
| 21.06 | 67.07 | 1004.9 | 84.09 | 446.44 | 446.2148995417375 |
| 21.08 | 44.05 | 1008.13 | 72.52 | 449.6 | 453.8663667211949 |
| 21.11 | 58.66 | 1011.7 | 68.71 | 457.89 | 451.0124137711604 |
| 21.11 | 59.39 | 1013.87 | 85.29 | 446.02 | 448.5883814092364 |
| 21.13 | 51.43 | 1007.43 | 88.72 | 445.4 | 449.5097304398748 |
| 21.14 | 58.05 | 1012.98 | 87.27 | 449.74 | 448.5033053489824 |
| 21.14 | 58.98 | 1009.05 | 94.36 | 448.31 | 446.9172206057899 |
| 21.16 | 45.38 | 1014.65 | 73.06 | 458.63 | 453.8324720669487 |
| 21.18 | 44.57 | 1007.27 | 73.67 | 449.93 | 453.30606496401464 |
| 21.18 | 60.1 | 1011.02 | 78.19 | 452.39 | 449.08008122232087 |
| 21.19 | 74.93 | 1015.75 | 80.84 | 443.29 | 445.36190546480196 |
| 21.26 | 50.32 | 1008.41 | 87.22 | 446.22 | 449.83432112958235 |
| 21.28 | 70.32 | 1011.06 | 90.12 | 439.0 | 444.60209183702824 |
| 21.32 | 45.01 | 1012.23 | 59.94 | 452.75 | 455.3330390953461 |
| 21.32 | 49.02 | 1008.81 | 85.81 | 445.65 | 450.28095549994475 |
| 21.33 | 58.46 | 1016.17 | 79.73 | 445.27 | 449.3942290809264 |
| 21.33 | 60.1 | 1010.97 | 78.36 | 451.95 | 448.76188428428145 |
| 21.33 | 63.86 | 1020.33 | 72.13 | 445.02 | 449.49526226001734 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 | 449.4914112072868 |
| 21.36 | 58.95 | 1018.35 | 78.87 | 443.93 | 449.5171242611325 |
| 21.36 | 68.28 | 1007.6 | 72.37 | 445.91 | 447.2640843894497 |
| 21.38 | 44.05 | 1005.69 | 81.66 | 445.71 | 451.75573664689705 |
| 21.39 | 51.3 | 1013.39 | 89.05 | 452.7 | 449.47769197848703 |
| 21.39 | 63.9 | 1013.44 | 70.95 | 449.38 | 448.9807967933042 |
| 21.4 | 44.57 | 1005.7 | 73.09 | 445.09 | 452.83851864090616 |
| 21.4 | 68.28 | 1008.2 | 60.34 | 450.22 | 448.99070865761826 |
| 21.41 | 56.9 | 1007.03 | 79.41 | 441.41 | 448.9314689751323 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 | 458.19122302999057 |
| 21.44 | 51.19 | 1009.1 | 84.94 | 446.17 | 449.6590005617065 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 | 446.545237358833 |
| 21.45 | 45.09 | 1013.83 | 52.26 | 453.15 | 456.3129527756159 |
| 21.45 | 60.08 | 1017.92 | 72.7 | 451.49 | 449.9268730104538 |
| 21.45 | 66.05 | 1014.81 | 73.73 | 453.38 | 448.0350183689646 |
| 21.46 | 46.63 | 1012.97 | 71.29 | 452.1 | 453.06361431502756 |
| 21.47 | 58.79 | 1017.0 | 76.97 | 446.33 | 449.51211271813366 |
| 21.49 | 56.9 | 1007.47 | 66.66 | 452.46 | 450.672947544889 |
| 21.52 | 66.51 | 1015.32 | 72.85 | 444.87 | 447.9552056621943 |
| 21.53 | 52.84 | 1005.06 | 88.22 | 444.04 | 448.2666586698033 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 | 448.66968221055265 |
| 21.54 | 69.48 | 1011.04 | 80.48 | 443.15 | 445.7146703190735 |
| 21.55 | 44.57 | 1006.03 | 71.54 | 446.84 | 452.8021698612517 |
| 21.55 | 60.27 | 1017.42 | 92.59 | 443.93 | 446.7443660008083 |
| 21.57 | 66.49 | 1014.76 | 68.19 | 455.18 | 448.49796091182566 |
| 21.58 | 63.87 | 1015.27 | 63.15 | 451.88 | 449.90863388073797 |
| 21.61 | 41.54 | 1014.5 | 75.62 | 458.47 | 453.53620348431275 |
| 21.61 | 49.39 | 1019.41 | 78.2 | 449.26 | 451.60241101975805 |
| 21.62 | 50.05 | 1007.2 | 92.9 | 444.16 | 448.28015134415546 |
| 21.65 | 58.18 | 1008.33 | 95.28 | 441.05 | 445.940139097113 |
| 21.69 | 44.57 | 1005.84 | 71.53 | 447.88 | 452.5181226448485 |
| 21.71 | 65.27 | 1013.24 | 63.58 | 444.91 | 449.080854354919 |
| 21.73 | 69.05 | 1001.31 | 86.64 | 439.01 | 443.764677908941 |
| 21.75 | 59.8 | 1016.65 | 72.94 | 453.17 | 449.2796285666682 |
Now that we have real predictions we can use an evaluation metric such as Root Mean Squared Error to validate our regression model. The lower the Root Mean Squared Error, the better our model.
//Now let's compute some evaluation metrics against our test dataset
import org.apache.spark.mllib.evaluation.RegressionMetrics
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))
import org.apache.spark.mllib.evaluation.RegressionMetrics
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@2cb97126
val rmse = metrics.rootMeanSquaredError
rmse: Double = 4.426730543741181
val explainedVariance = metrics.explainedVariance
explainedVariance: Double = 269.3177257387032
val r2 = metrics.r2
r2: Double = 0.9314313152952873
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 4.426730543741181
Explained Variance: 269.3177257387032
R2: 0.9314313152952873
Generally a good model will have 68% of predictions within 1 RMSE and 95% within 2 RMSE of the actual value. Let's calculate and see if our RMSE meets this criteria.
display(predictionsAndLabels) // recall the DataFrame predictionsAndLabels
// First we calculate the residual error and divide it by the RMSE from predictionsAndLabels DataFrame and make another DataFrame that is registered as a temporary table Power_Plant_RMSE_Evaluation
predictionsAndLabels.selectExpr("PE", "Predicted_PE", "PE - Predicted_PE AS Residual_Error", s""" (PE - Predicted_PE) / $rmse AS Within_RSME""").createOrReplaceTempView("Power_Plant_RMSE_Evaluation")
SELECT * from Power_Plant_RMSE_Evaluation
| PE | Predicted_PE | Residual_Error | Within_RSME |
|---|---|---|---|
| 490.34 | 493.23742177145215 | -2.897421771452173 | -0.6545286058914855 |
| 482.66 | 488.93663844863084 | -6.27663844863082 | -1.4178948518800556 |
| 489.04 | 488.2642491377776 | 0.7757508622224236 | 0.17524239493619823 |
| 489.64 | 487.68500173970443 | 1.9549982602955538 | 0.44163480044197995 |
| 489.0 | 487.8432005878593 | 1.1567994121406855 | 0.26132139752130357 |
| 488.03 | 486.7875685475632 | 1.24243145243679 | 0.28066570579802425 |
| 491.9 | 485.8682911927764 | 6.031708807223595 | 1.3625651590092023 |
| 489.36 | 485.34119715268844 | 4.018802847311576 | 0.9078489886839033 |
| 481.47 | 482.9938172155284 | -1.5238172155283678 | -0.3442308494884213 |
| 482.05 | 482.5648390621137 | -0.5148390621137082 | -0.11630232674577932 |
| 494.75 | 487.00024243767876 | 7.749757562321236 | 1.7506729821805804 |
| 482.98 | 483.3467525779819 | -0.3667525779818561 | -8.284953745386567e-2 |
| 475.34 | 482.8336530053327 | -7.493653005332703 | -1.692818871916148 |
| 483.73 | 483.6145343498689 | 0.11546565013111376 | 2.608373132048146e-2 |
| 488.42 | 484.53187599470635 | 3.888124005293662 | 0.8783285919200484 |
| 495.35 | 487.2657538698361 | 8.084246130163933 | 1.8262340682999108 |
| 491.32 | 487.10787889447494 | 4.212121105525057 | 0.9515196517846441 |
| 486.46 | 482.0547750131424 | 4.405224986857604 | 0.9951418870719423 |
| 483.12 | 483.688095258955 | -0.5680952589549975 | -0.12833292050229034 |
| 495.23 | 487.0194077282659 | 8.210592271734129 | 1.8547757064958097 |
| 479.91 | 480.1986449575354 | -0.2886449575353822 | -6.520499829010114e-2 |
| 492.12 | 484.35541367676683 | 7.764586323233175 | 1.7540228045303743 |
| 491.38 | 483.7973981417879 | 7.582601858212115 | 1.712912449331917 |
| 481.56 | 484.07023605498495 | -2.5102360549849436 | -0.567063215206105 |
| 491.84 | 484.05572584065357 | 7.78427415934641 | 1.758470293691663 |
| 484.64 | 483.0334383183464 | 1.6065616816536021 | 0.36292285373571487 |
| 490.07 | 483.9952002249004 | 6.0747997750996205 | 1.3722994239368362 |
| 478.02 | 480.02034741363497 | -2.0003474136349837 | -0.4518791902667791 |
| 476.61 | 479.7602256710553 | -3.1502256710552956 | -0.711637096481805 |
| 487.09 | 483.7798894431585 | 3.3101105568414937 | 0.7477551488923485 |
| 482.82 | 483.2217349280458 | -0.401734928045812 | -9.075206274161249e-2 |
| 481.6 | 480.1171178824119 | 1.482882117588133 | 0.33498359634397323 |
| 470.02 | 482.0478125504672 | -12.027812550467218 | -2.717087121436152 |
| 489.05 | 481.81327159305386 | 7.236728406946156 | 1.6347795140090344 |
| 487.03 | 484.6333949755666 | 2.39660502443337 | 0.5413939250993844 |
| 490.57 | 482.2826790358646 | 8.287320964135404 | 1.8721087453250556 |
| 488.57 | 482.86050781997415 | 5.709492180025848 | 1.2897763086344445 |
| 480.39 | 483.17821215232124 | -2.7882121523212504 | -0.6298581141929721 |
| 481.37 | 483.0829476732433 | -1.7129476732433204 | -0.3869554869711247 |
| 489.62 | 482.71830544679335 | 6.901694553206653 | 1.5590952476122018 |
| 481.13 | 481.35862683823984 | -0.22862683823984753 | -5.164688385271067e-2 |
| 485.94 | 481.4164995749685 | 4.5235004250315 | 1.021860350507925 |
| 482.49 | 482.3037528502627 | 0.18624714973731216 | 4.2073297187840204e-2 |
| 483.77 | 483.6070229944035 | 0.1629770055964741 | 3.6816563372465104e-2 |
| 491.77 | 481.0249164343975 | 10.7450835656025 | 2.4273181887690556 |
| 483.43 | 482.2081944229683 | 1.2218055770317164 | 0.2760063132279849 |
| 481.49 | 482.6905244198958 | -1.2005244198957712 | -0.2711988922825122 |
| 478.78 | 480.9741288614041 | -2.194128861404124 | -0.49565448805243767 |
| 492.96 | 485.8565717694332 | 7.103428230566806 | 1.6046669568831393 |
| 481.36 | 483.99243959507345 | -2.6324395950734356 | -0.5946690382578993 |
| 486.68 | 481.76650494734884 | 4.913495052651172 | 1.1099602752189677 |
| 484.94 | 483.069724719673 | 1.8702752803270073 | 0.42249584921569994 |
| 483.01 | 481.33834961288795 | 1.671650387112038 | 0.3776264153858503 |
| 488.17 | 483.1883946104104 | 4.9816053895896175 | 1.1253464244922151 |
| 490.41 | 480.1679106982307 | 10.24208930176934 | 2.313691606156222 |
| 483.11 | 482.4149038683481 | 0.6950961316518942 | 0.15702246269194528 |
| 485.89 | 480.94068220101354 | 4.949317798986442 | 1.1180526463224945 |
| 487.08 | 482.508645049916 | 4.571354950083958 | 1.0326707046913566 |
| 477.8 | 480.01746628251317 | -2.2174662825131577 | -0.5009264197587914 |
| 489.96 | 480.63940670945976 | 9.320593290540216 | 2.1055253303633577 |
| 486.92 | 483.0108446487773 | 3.9091553512226938 | 0.883079580425271 |
| 486.37 | 482.57972730795177 | 3.7902726920482337 | 0.8562239455498787 |
| 488.2 | 483.51586402481416 | 4.684135975185825 | 1.0581479782654901 |
| 479.06 | 481.6634377951154 | -2.603437795115383 | -0.5881175213603873 |
| 480.05 | 484.68450470880435 | -4.634504708804343 | -1.0469362575856187 |
| 487.18 | 482.8218608903564 | 4.358139109643616 | 0.9845051707078616 |
| 480.47 | 481.8880244206695 | -1.4180244206694965 | -0.32033221960491765 |
| 480.36 | 480.6306788292839 | -0.2706788292838951 | -6.11464435454739e-2 |
| 487.85 | 479.89671820679695 | 7.953281793203075 | 1.7966491781271794 |
| 486.11 | 479.6914116057231 | 6.418588394276924 | 1.449961394951398 |
| 481.83 | 481.1970697561745 | 0.6329302438254558 | 0.1429791665816065 |
| 482.96 | 482.17217802621536 | 0.7878219737846166 | 0.17796926331974147 |
| 483.92 | 481.3074409763495 | 2.612559023650533 | 0.590178010121793 |
| 477.69 | 479.64992318380445 | -1.9599231838044489 | -0.4427473424095181 |
| 474.25 | 480.70714895561014 | -6.457148955610137 | -1.4586722394340677 |
| 479.08 | 479.7024675196716 | -0.6224675196716021 | -0.14061563348410486 |
| 478.89 | 481.00544489343537 | -2.115444893435381 | -0.4778797517789611 |
| 483.11 | 482.38901084355183 | 0.7209891564481836 | 0.16287170617772706 |
| 488.43 | 481.25646633043965 | 7.173533669560356 | 1.6205038004183012 |
| 483.28 | 480.33371483717946 | 2.946285162820516 | 0.6655668633335223 |
| 486.76 | 482.5311759738776 | 4.228824026122368 | 0.9552928474721311 |
| 476.58 | 480.67721106043905 | -4.097211060439065 | -0.9255614318409298 |
| 481.61 | 482.53257783094546 | -0.9225778309454427 | -0.20841065925050425 |
| 477.8 | 480.0330510466423 | -2.233051046642288 | -0.5044470235035043 |
| 475.32 | 479.3324132109004 | -4.012413210900434 | -0.9064055675522111 |
| 475.89 | 478.2499419685471 | -2.359941968547105 | -0.5331117277702286 |
| 485.03 | 480.3355565472517 | 4.69444345274826 | 1.0604764411029242 |
| 482.46 | 479.7315598782737 | 2.728440121726294 | 0.6163555912803304 |
| 484.57 | 477.91894784424176 | 6.6510521557582365 | 1.502475041125319 |
| 483.94 | 483.45191519870116 | 0.48808480129883947 | 0.11025852973791857 |
| 482.39 | 482.34452319301437 | 4.547680698561862e-2 | 1.027322682875218e-2 |
| 483.8 | 478.45760011663003 | 5.342399883369978 | 1.2068500286116204 |
| 482.22 | 478.7638216096892 | 3.4561783903108108 | 0.7807519242835766 |
| 481.52 | 479.1388800137473 | 2.3811199862526564 | 0.5378958494817917 |
| 483.79 | 477.1846287648614 | 6.605371235138648 | 1.4921557049542535 |
| 480.54 | 476.81117998099177 | 3.7288200190082534 | 0.8423417649127792 |
| 477.27 | 480.84424359067077 | -3.5742435906707897 | -0.8074228949228237 |
| 474.5 | 480.1364995691749 | -5.636499569174873 | -1.273287251952606 |
| 475.52 | 479.2235127059344 | -3.703512705934429 | -0.8366248339128552 |
| 481.44 | 478.91505388939413 | 2.5249461106058675 | 0.5703862219885535 |
| 480.6 | 476.210862698602 | 4.389137301398023 | 0.9915076732202934 |
| 487.19 | 481.0254321330136 | 6.164567866986374 | 1.392578067734949 |
| 475.42 | 480.36098930984286 | -4.940989309842848 | -1.1161712376708273 |
| 476.22 | 478.51210495606927 | -2.292104956069238 | -0.5177873225895746 |
| 485.13 | 479.57731721621883 | 5.552682783781165 | 1.2543530104022107 |
| 488.02 | 484.33140555054337 | 3.688594449456616 | 0.8332547944829863 |
| 477.78 | 478.4450809561189 | -0.6650809561189135 | -0.15024202389261102 |
| 477.2 | 480.5031526805204 | -3.303152680520384 | -0.7461833621634392 |
| 467.56 | 479.2169410835439 | -11.65694108354387 | -2.6333071255094267 |
| 485.18 | 477.6146348725092 | 7.565365127490793 | 1.7090186657480726 |
| 483.52 | 478.33312476559934 | 5.186875234400645 | 1.17171695524459 |
| 480.21 | 479.4778900760471 | 0.7321099239528621 | 0.16538389150158914 |
| 478.81 | 481.48536176155926 | -2.675361761559259 | -0.6043651708916123 |
| 484.67 | 477.9675154808001 | 6.702484519199913 | 1.5140936302699408 |
| 477.9 | 479.6817134321857 | -1.781713432185711 | -0.40248969630754267 |
| 475.17 | 477.5050067316079 | -2.335006731607905 | -0.5274788489010923 |
| 485.73 | 478.32045063849523 | 7.409549361504787 | 1.673819828943716 |
| 479.91 | 478.1399555772117 | 1.7700444227883168 | 0.3998536629456538 |
| 486.4 | 479.65037308403333 | 6.749626915966644 | 1.5247431144210337 |
| 480.15 | 477.1324329554068 | 3.017567044593193 | 0.681669465709775 |
| 480.58 | 482.82343628916425 | -2.243436289164265 | -0.5067930534728822 |
| 484.07 | 478.16947013024355 | 5.90052986975644 | 1.3329317905059794 |
| 481.89 | 480.6437911412516 | 1.246208858748389 | 0.2815190232236669 |
| 483.14 | 478.8928744938167 | 4.247125506183295 | 0.9594271583094607 |
| 479.31 | 476.1246111330079 | 3.1853888669921275 | 0.7195804749163808 |
| 484.21 | 477.288235770853 | 6.921764229146959 | 1.563628994525865 |
| 476.02 | 478.30600580479216 | -2.2860058047921825 | -0.5164095221527084 |
| 480.69 | 478.2957350932866 | 2.3942649067133743 | 0.5408652916763936 |
| 481.91 | 476.52270993699136 | 5.387290063008663 | 1.2169907361146226 |
| 485.06 | 478.99917896902446 | 6.060821030975546 | 1.3691416206809235 |
| 485.29 | 480.8674382556021 | 4.42256174439791 | 0.9990582667496749 |
| 482.39 | 481.1210453702997 | 1.2689546297002607 | 0.2866573009496584 |
| 478.25 | 478.8354389662744 | -0.5854389662744097 | -0.1322508701375881 |
| 475.79 | 476.29244393984663 | -0.5024439398466143 | -0.11350226422907182 |
| 485.87 | 479.0178844308566 | 6.852115569143393 | 1.5478953375265612 |
| 479.23 | 476.4582419334783 | 2.771758066521727 | 0.6261411303745675 |
| 478.61 | 480.1903466285615 | -1.5803466285614718 | -0.3570008639436786 |
| 479.94 | 479.4487267515188 | 0.4912732484812068 | 0.11097880108736301 |
| 479.25 | 479.3384367489267 | -8.84367489267106e-2 | -1.997789295120947e-2 |
| 480.99 | 477.44189376811596 | 3.5481062318840486 | 0.8015184563019332 |
| 481.07 | 476.33561360755584 | 4.734386392444151 | 1.0694995653480546 |
| 480.4 | 482.3769169335795 | -1.9769169335795027 | -0.44658623651141477 |
| 482.8 | 476.04420182940044 | 6.755798170599576 | 1.5261372030315674 |
| 480.08 | 479.65335282852305 | 0.42664717147692954 | 9.637974736911713e-2 |
| 475.0 | 478.6696951676176 | -3.66969516761759 | -0.8289854400119429 |
| 483.88 | 476.696926422392 | 7.183073577608013 | 1.6226588690300885 |
| 482.52 | 476.90393713153463 | 5.616062868465349 | 1.2686705940133918 |
| 478.45 | 479.2188844607746 | -0.7688844607745864 | -0.17369127241360752 |
| 482.3 | 475.81261283946816 | 6.487387160531853 | 1.465503060651426 |
| 472.45 | 476.2100211409317 | -3.7600211409317126 | -0.8493901094223798 |
| 480.2 | 480.2141595165933 | -1.415951659333814e-2 | -3.19863982083975e-3 |
| 478.38 | 474.9481764100585 | 3.431823589941473 | 0.775250166241454 |
| 472.77 | 479.30598211413803 | -6.535982114138051 | -1.476480677907779 |
| 483.53 | 476.3744577455605 | 7.155542254439467 | 1.6164395333609067 |
| 475.47 | 478.6980048877349 | -3.228004887734869 | -0.7292074491181413 |
| 467.24 | 476.41215657483474 | -9.17215657483473 | -2.0719934236347326 |
| 481.03 | 480.7338755379764 | 0.29612446202355613 | 6.689462100697262e-2 |
| 482.37 | 480.51130475015583 | 1.8586952498441747 | 0.41987991622217147 |
| 484.97 | 478.0206284049 | 6.949371595100047 | 1.5698655082870474 |
| 479.53 | 475.4980717304681 | 4.031928269531875 | 0.9108140262190783 |
| 482.8 | 476.4769333498689 | 6.323066650131125 | 1.4283829990671366 |
| 477.26 | 480.5658974037096 | -3.3058974037095936 | -0.7468033961054397 |
| 479.02 | 478.9378167534134 | 8.218324658656684e-2 | 1.8565224554443056e-2 |
| 476.69 | 479.483969889582 | -2.7939698895820015 | -0.631158789082455 |
| 476.62 | 476.72728884411293 | -0.10728884411292938 | -2.423658794064658e-2 |
| 475.69 | 475.1283411969007 | 0.5616588030993057 | 0.1268789228414677 |
| 482.95 | 477.43108128919135 | 5.518918710808634 | 1.2467256943415417 |
| 483.24 | 479.0201634085648 | 4.2198365914351825 | 0.9532625827884375 |
| 484.86 | 476.63324412261045 | 8.226755877389564 | 1.858427070746631 |
| 491.97 | 478.3337308000573 | 13.636269199942717 | 3.080438049074982 |
| 474.88 | 478.39283560851754 | -3.5128356085175483 | -0.7935508099728905 |
| 475.64 | 477.10087603877 | -1.4608760387700386 | -0.33001241533337206 |
| 484.96 | 478.16396362897893 | 6.796036371021046 | 1.5352270267793358 |
| 475.42 | 478.96772021173297 | -3.547720211732951 | -0.8014312542128782 |
| 480.38 | 475.6006326388746 | 4.7793673611254235 | 1.0796607821279804 |
| 478.82 | 474.9766634864767 | 3.8433365135232975 | 0.8682110816429235 |
| 482.55 | 478.786077505979 | 3.763922494021017 | 0.8502714264690703 |
| 480.14 | 478.9365615888623 | 1.2034384111377108 | 0.271857163937664 |
| 482.55 | 476.1723094438278 | 6.377690556172183 | 1.4407225588170043 |
| 477.91 | 478.2490255806092 | -0.33902558060918864 | -7.658599891256687e-2 |
| 476.25 | 474.4577268339357 | 1.7922731660643194 | 0.4048751439362759 |
| 471.13 | 478.43777544278043 | -7.307775442780439 | -1.6508290646045036 |
| 482.16 | 478.00197412694763 | 4.158025873052395 | 0.9392995195813987 |
| 480.87 | 479.3152324207446 | 1.5547675792553832 | 0.3512225476325008 |
| 477.34 | 477.2801935898294 | 5.980641017055177e-2 | 1.3510289270963245e-2 |
| 483.66 | 478.285031656868 | 5.374968343132025 | 1.214207255223955 |
| 481.95 | 475.9420528274367 | 6.007947172563263 | 1.357197397311141 |
| 479.68 | 476.17198214059135 | 3.5080178594086533 | 0.7924624787403274 |
| 478.66 | 474.34503134979343 | 4.314968650206595 | 0.9747529486084029 |
| 478.8 | 479.7691576930105 | -0.9691576930105157 | -0.2189330666129607 |
| 481.12 | 475.82678857769963 | 5.293211422300374 | 1.1957383378087658 |
| 476.54 | 478.0366119605891 | -1.496611960589064 | -0.33808517274788225 |
| 478.03 | 477.46151999839185 | 0.5684800016081226 | 0.12841983400410018 |
| 479.23 | 479.110912113517 | 0.1190878864830438 | 2.6901995797195863e-2 |
| 480.74 | 477.72481326338215 | 3.0151867366178635 | 0.6811317532938489 |
| 478.88 | 479.54303529435083 | -0.6630352943508342 | -0.14977990817360218 |
| 481.05 | 479.71268358186353 | 1.3373164181364814 | 0.3021002532054435 |
| 473.54 | 473.26068816423447 | 0.2793118357655544 | 6.309664277182285e-2 |
| 469.73 | 476.05175958076205 | -6.321759580762034 | -1.4280877316330394 |
| 475.51 | 476.88388448180046 | -1.3738844818004736 | -0.31036099175789383 |
| 476.67 | 475.61433131158714 | 1.0556686884128794 | 0.23847593115995214 |
| 472.16 | 477.7130066863276 | -5.553006686327592 | -1.2544261801023373 |
| 481.85 | 475.2673812565115 | 6.582618743488524 | 1.487015909020143 |
| 479.45 | 479.3846135509556 | 6.538644904441071e-2 | 1.4770822031817277e-2 |
| 482.38 | 475.5344685772051 | 6.84553142279492 | 1.5464079765310332 |
| 469.58 | 473.85672752722735 | -4.276727527227365 | -0.9661142653632034 |
| 477.93 | 477.3826135030455 | 0.5473864969545161 | 0.12365480382094843 |
| 478.21 | 477.2300569616709 | 0.9799430383290542 | 0.2213694799459989 |
| 477.62 | 479.265495182268 | -1.6454951822680073 | -0.371717945334288 |
| 472.46 | 472.7773808573311 | -0.3173808573311021 | -7.16964482466269e-2 |
| 467.37 | 473.2887424901299 | -5.918742490129887 | -1.3370460279083884 |
| 473.2 | 475.32128019247375 | -2.121280192473762 | -0.4791979479015218 |
| 480.05 | 476.83702080523557 | 3.2129791947644435 | 0.7258131397464832 |
| 469.65 | 473.90133694043874 | -4.251336940438762 | -0.9603785228015735 |
| 485.23 | 477.4312500724956 | 7.798749927504446 | 1.761740374853143 |
| 477.88 | 474.53026960482526 | 3.3497303951747313 | 0.756705284425051 |
| 475.21 | 475.5632280329792 | -0.35322803297924565 | -7.97943379405968e-2 |
| 475.91 | 476.45899184845075 | -0.5489918484507257 | -0.12401745329336308 |
| 477.85 | 475.16451288467897 | 2.6854871153210524 | 0.6066524918978817 |
| 464.86 | 472.95056743270436 | -8.090567432704347 | -1.827662052785967 |
| 466.05 | 473.09691475779204 | -7.046914757792024 | -1.591900543337891 |
| 463.16 | 472.93274352761154 | -9.77274352761151 | -2.2076662292962226 |
| 475.75 | 478.4561215361668 | -2.7061215361667905 | -0.6113138148859982 |
| 471.6 | 474.6981382928799 | -3.0981382928798666 | -0.6998705392764937 |
| 470.82 | 475.02803269176394 | -4.208032691763947 | -0.950596077665842 |
| 479.63 | 475.85397168574394 | 3.7760283142560525 | 0.8530061355541199 |
| 477.68 | 479.18053767427625 | -1.500537674276245 | -0.3389719928622738 |
| 478.73 | 473.5654621009553 | 5.164537899044717 | 1.166670943264595 |
| 473.66 | 477.1725989266351 | -3.5125989266350643 | -0.793497343451686 |
| 474.28 | 475.0636358283201 | -0.7836358283201434 | -0.17702361157448407 |
| 468.19 | 473.5875494551498 | -5.397549455149829 | -1.2193083364383357 |
| 463.57 | 472.67682233352394 | -9.10682233352395 | -2.0572343953484604 |
| 475.46 | 476.9235641778536 | -1.463564177853641 | -0.3306196669058454 |
| 473.05 | 471.68772310073444 | 1.3622768992655665 | 0.30773883474602903 |
| 474.92 | 475.9474342905188 | -1.0274342905187837 | -0.23209777066088685 |
| 481.31 | 474.932278891215 | 6.3777211087850105 | 1.4407294606630792 |
| 473.43 | 474.55112952359036 | -1.121129523590355 | -0.25326355704561365 |
| 477.27 | 477.46636654211125 | -0.1963665421112637 | -4.4359271514481574e-2 |
| 476.91 | 473.8650937482109 | 3.0449062517891434 | 0.6878454023126037 |
| 468.27 | 473.17098851617544 | -4.90098851617546 | -1.1071350441930146 |
| 480.11 | 474.8258713039414 | 5.284128696058588 | 1.1936865467290878 |
| 471.79 | 473.1211730372522 | -1.3311730372521993 | -0.3007124612846165 |
| 480.87 | 474.62974157133897 | 6.240258428661036 | 1.4096765924648265 |
| 477.53 | 474.801072598715 | 2.7289274012849773 | 0.6164656679054759 |
| 476.03 | 471.6665106288944 | 4.3634893711055724 | 0.9857137966698644 |
| 479.28 | 474.5250337253637 | 4.754966274636274 | 1.0741485680349747 |
| 470.76 | 473.3969220129792 | -2.6369220129791984 | -0.5956816180527323 |
| 477.65 | 475.74847822607404 | 1.9015217739259356 | 0.42955444320288233 |
| 474.16 | 472.3991408528041 | 1.7608591471959016 | 0.3977787059312943 |
| 476.31 | 477.2616855096003 | -0.9516855096002814 | -0.21498609418317555 |
| 479.17 | 474.9177051337058 | 4.252294866294221 | 0.9605949185920998 |
| 473.16 | 477.4228902388337 | -4.262890238833677 | -0.9629884170069595 |
| 478.03 | 473.50046202531144 | 4.529537974688537 | 1.0232242351169787 |
| 470.66 | 473.24796901874475 | -2.5879690187447295 | -0.5846231192914554 |
| 479.14 | 473.09058493481064 | 6.049415065189351 | 1.3665650089641064 |
| 480.04 | 472.3076103285209 | 7.732389671479098 | 1.746749569478921 |
| 472.54 | 471.615832151066 | 0.9241678489340188 | 0.20876984487810568 |
| 479.24 | 473.69713759778955 | 5.54286240221046 | 1.2521345827220822 |
| 474.42 | 472.19156900447234 | 2.228430995527674 | 0.5034033523179731 |
| 477.41 | 476.3350912306915 | 1.0749087693085357 | 0.2428222722587704 |
| 472.16 | 475.77435376625584 | -3.6143537662558174 | -0.8164837978146291 |
| 476.55 | 472.6471168581127 | 3.902883141887287 | 0.8816626861116392 |
| 474.24 | 472.62895527440253 | 1.6110447255974805 | 0.3639355749527803 |
| 474.91 | 472.5658087964315 | 2.3441912035685277 | 0.529553624374745 |
| 474.94 | 477.2343522450378 | -2.294352245037828 | -0.5182949859646964 |
| 478.47 | 473.38659302581107 | 5.083406974188961 | 1.1483434385624023 |
| 481.3 | 473.3858358704527 | 7.914164129547316 | 1.7878124840322416 |
| 477.19 | 472.2522916899094 | 4.937708310090613 | 1.1154300586630213 |
| 479.61 | 471.9871102146372 | 7.6228897853628155 | 1.7220135063654567 |
| 474.03 | 476.66325912606675 | -2.6332591260667755 | -0.5948541705999838 |
| 484.94 | 473.0585846959978 | 11.88141530400219 | 2.6840159315323495 |
| 478.76 | 472.5409240450936 | 6.219075954906373 | 1.4048914641301884 |
| 477.23 | 472.5905086170794 | 4.639491382920596 | 1.048062749037262 |
| 477.93 | 473.2433331311532 | 4.686666868846828 | 1.0587197080412232 |
| 483.54 | 473.33367076102724 | 10.206329238972785 | 2.305613395286325 |
| 470.66 | 472.25860679152254 | -1.5986067915225135 | -0.36112584123348884 |
| 468.57 | 472.68332438968736 | -4.113324389687364 | -0.9292014386335458 |
| 475.46 | 469.26491536026253 | 6.195084639737445 | 1.3994718175237673 |
| 467.6 | 476.77045249286635 | -9.170452492866332 | -2.07160847091363 |
| 479.16 | 472.2986932100967 | 6.861306789903324 | 1.5499716375563712 |
| 473.72 | 475.9280130742943 | -2.2080130742942856 | -0.49879093666907914 |
| 470.9 | 474.1703211862971 | -3.27032118629711 | -0.7387667159730148 |
| 479.2 | 473.8182300033406 | 5.381769996659386 | 1.215743751168344 |
| 476.32 | 474.4834318795844 | 1.8365681204156203 | 0.41488138983573053 |
| 477.34 | 473.75018039571677 | 3.589819604283207 | 0.8109415219227073 |
| 472.34 | 471.57861538146454 | 0.7613846185354305 | 0.17199705539157537 |
| 473.29 | 471.6415723647869 | 1.6484276352131246 | 0.37238038749473606 |
| 477.3 | 472.7701885173113 | 4.529811482688729 | 1.0232860206712358 |
| 472.11 | 471.77143688745184 | 0.3385631125481723 | 7.648152721354506e-2 |
| 468.09 | 474.5636411763966 | -6.4736411763965975 | -1.462397837959548 |
| 477.62 | 472.7148284274264 | 4.905171572573579 | 1.1080799981170868 |
| 468.84 | 472.4884135100371 | -3.6484135100371304 | -0.8241779060158768 |
| 477.2 | 474.2579394355058 | 2.94206056449417 | 0.6646125250731286 |
| 473.16 | 475.5315818680234 | -2.3715818680233838 | -0.5357411851906123 |
| 467.46 | 472.3352405654698 | -4.875240565469824 | -1.1013185729957693 |
| 476.24 | 471.7121476384473 | 4.527852361552732 | 1.0228434545117104 |
| 462.07 | 471.87019509945225 | -9.800195099452253 | -2.2138675491121655 |
| 478.3 | 473.39040211351204 | 4.909597886487973 | 1.1090799040003696 |
| 474.64 | 472.2988980097268 | 2.3411019902731596 | 0.528855769995572 |
| 476.18 | 473.7408434668035 | 2.439156533196524 | 0.5510063260220736 |
| 472.02 | 474.7548947976392 | -2.7348947976392424 | -0.6178137048585499 |
| 468.75 | 475.5773739728955 | -6.8273739728954865 | -1.5423062021582727 |
| 476.87 | 476.2403822241514 | 0.629617775848601 | 0.14223087889069697 |
| 472.27 | 469.24091010473035 | 3.0290898952696352 | 0.6842724817647582 |
| 476.7 | 472.7393314749417 | 3.9606685250582814 | 0.8947164246665408 |
| 473.93 | 472.63331852543564 | 1.296681474564366 | 0.2929208050392189 |
| 476.04 | 471.38775711954423 | 4.652242880455788 | 1.0509433168534397 |
| 471.92 | 475.2099855538805 | -3.289985553880456 | -0.7432089035850772 |
| 469.9 | 471.84963289726664 | -1.9496328972666674 | -0.4404227630306511 |
| 467.19 | 474.9342554366788 | -7.744255436678827 | -1.749430050046347 |
| 464.07 | 472.0765967355643 | -8.006596735564301 | -1.8086930425174812 |
| 472.45 | 477.1428353332941 | -4.692835333294113 | -1.0601131663478296 |
| 475.51 | 470.47813470324115 | 5.031865296758838 | 1.1367001553490168 |
| 476.91 | 473.32755876405025 | 3.582441235949773 | 0.8092747458990647 |
| 469.04 | 472.10000669812564 | -3.060006698125619 | -0.6912565984961676 |
| 474.49 | 471.88884153658796 | 2.601158463412048 | 0.5876026195201212 |
| 474.29 | 471.68655893301997 | 2.6034410669800536 | 0.588118260475777 |
| 465.61 | 472.62327183365306 | -7.01327183365305 | -1.5843005948416944 |
| 477.71 | 472.2836129719507 | 5.426387028049305 | 1.2258227543850637 |
| 460.6 | 470.4334505230224 | -9.83345052302235 | -2.221379961092406 |
| 474.72 | 471.91129640538503 | 2.8087035946149967 | 0.634487138275479 |
| 476.89 | 474.3054501914308 | 2.584549808569193 | 0.5838507185000019 |
| 471.56 | 474.6794786091428 | -3.1194786091427886 | -0.7046913242897344 |
| 473.54 | 475.187496898361 | -1.6474968983609983 | -0.37217013370970675 |
| 467.96 | 469.771457326924 | -1.8114573269240282 | -0.40920885267913865 |
| 475.13 | 472.08490735121984 | 3.0450926487801553 | 0.6878875094589886 |
| 470.88 | 473.8032225628117 | -2.923222562811702 | -0.6603570138111878 |
| 474.22 | 472.09588182193215 | 2.1241181780678744 | 0.47983904985387016 |
| 476.41 | 471.4926212025492 | 4.917378797450851 | 1.1108376145467862 |
| 465.45 | 471.19014476340215 | -5.7401447634021565 | -1.2967007380916762 |
| 478.51 | 471.43677609572336 | 7.073223904276631 | 1.597843788860663 |
| 464.43 | 469.7436046712689 | -5.313604671268877 | -1.2003451799842708 |
| 475.19 | 471.72684171223557 | 3.463158287764429 | 0.7823286855941306 |
| 476.12 | 474.3311768410223 | 1.7888231589777206 | 0.4040957861116898 |
| 466.52 | 470.11266519044227 | -3.5926651904422897 | -0.8115843408453783 |
| 466.75 | 469.0361408145477 | -2.286140814547707 | -0.5164400209043697 |
| 476.97 | 474.02676004123384 | 2.943239958766185 | 0.6648789506575099 |
| 476.73 | 470.9633331252549 | 5.766666874745113 | 1.302692092451488 |
| 473.82 | 471.12775460619287 | 2.692245393807127 | 0.6081791894050589 |
| 478.29 | 473.91084477665686 | 4.379155223343162 | 0.9892527182470402 |
| 473.79 | 470.2438958288006 | 3.546104171199431 | 0.8010661900831437 |
| 477.75 | 471.7153938676623 | 6.034606132337672 | 1.3632196657801583 |
| 470.33 | 474.55914246739445 | -4.2291424673944675 | -0.95536478346845 |
| 474.57 | 469.8009518761225 | 4.769048123877496 | 1.0773296627734226 |
| 464.57 | 470.2642322610151 | -5.694232261015088 | -1.2863290875171947 |
| 469.34 | 471.3638012594579 | -2.023801259457912 | -0.45717742235729764 |
| 465.82 | 471.92094622344274 | -6.100946223442747 | -1.3782059158917384 |
| 477.74 | 471.85580587680386 | 5.884194123196153 | 1.3292415395636934 |
| 474.28 | 470.30107562853505 | 3.9789243714649274 | 0.898840426845182 |
| 464.14 | 466.34356012780466 | -2.2035601278046784 | -0.49778501447760914 |
| 454.18 | 466.58075506687766 | -12.40075506687765 | -2.8013349681766146 |
| 467.21 | 469.3396022995035 | -2.1296022995035173 | -0.4810779148314091 |
| 470.36 | 471.72756140636227 | -1.367561406362256 | -0.30893260677359485 |
| 476.84 | 470.5786344502193 | 6.261365549780692 | 1.4144446986124883 |
| 474.35 | 470.6068799512958 | 3.7431200487042133 | 0.8455721466933415 |
| 477.61 | 472.5235663152981 | 5.086433684701888 | 1.149027173540852 |
| 478.1 | 471.9097423001995 | 6.1902576998004974 | 1.398381410079887 |
| 462.1 | 471.2847044384862 | -9.184704438486165 | -2.07482799048434 |
| 474.82 | 471.2662319288046 | 3.5537680711954067 | 0.802797467810633 |
| 463.03 | 469.73593028388524 | -6.705930283885266 | -1.5148720297345803 |
| 472.68 | 470.7066911497908 | 1.9733088502092073 | 0.4457711691982717 |
| 474.91 | 471.33177180371854 | 3.5782281962814864 | 0.8083230187436264 |
| 468.91 | 470.52692515963395 | -1.6169251596339222 | -0.3652639670874125 |
| 464.45 | 470.2092170118331 | -5.75921701183313 | -1.301009165777192 |
| 466.83 | 466.2832533837298 | 0.546746616270184 | 0.12351025454739102 |
| 475.24 | 470.18594349686214 | 5.054056503137872 | 1.1417131567412993 |
| 473.84 | 475.4613835085186 | -1.6213835085186474 | -0.36627110968185583 |
| 475.13 | 474.5994035325814 | 0.5305964674186043 | 0.11986193019333387 |
| 476.42 | 472.0863918606857 | 4.333608139314322 | 0.9789636158092969 |
| 474.46 | 470.7913069417126 | 3.6686930582873742 | 0.8287590631587972 |
| 468.13 | 467.56407826412726 | 0.5659217358727346 | 0.12784192086705481 |
| 470.27 | 471.5687225134983 | -1.2987225134983191 | -0.2933818764583589 |
| 471.6 | 469.9595844589464 | 1.6404155410536418 | 0.37057045258220545 |
| 472.49 | 471.6990473850642 | 0.7909526149358044 | 0.17867647626623853 |
| 479.32 | 473.0943993730868 | 6.225600626913206 | 1.4063653898508444 |
| 471.65 | 470.00140680122996 | 1.6485931987700155 | 0.3724177883609634 |
| 474.71 | 472.62554215897904 | 2.084457841020935 | 0.47087976564737744 |
| 467.2 | 468.51057584459073 | -1.310575844590744 | -0.29605954815653446 |
| 468.37 | 466.99762401287654 | 1.372375987123462 | 0.3100202222752913 |
| 464.4 | 467.7515630344035 | -3.3515630344035117 | -0.7571192782768728 |
| 467.47 | 468.17545309508614 | -0.7054530950861135 | -0.159362104405368 |
| 468.43 | 466.2393815815799 | 2.190618418420115 | 0.4948614777371899 |
| 466.05 | 470.09910156010346 | -4.049101560103452 | -0.914693478650593 |
| 471.1 | 470.4126440262426 | 0.6873559737574055 | 0.1552739582781331 |
| 468.01 | 470.1081379355774 | -2.0981379355774266 | -0.47397010386004174 |
| 477.41 | 473.04817642574005 | 4.361823574259972 | 0.9853374925715822 |
| 469.56 | 470.7822892277393 | -1.2222892277392816 | -0.27611557009437565 |
| 467.18 | 467.70575905298347 | -0.5257590529834602 | -0.11876915655659566 |
| 461.35 | 469.8226213597647 | -8.472621359764673 | -1.9139681704240736 |
| 474.4 | 474.3780743285961 | 2.192567140389201e-2 | 4.953016947212214e-3 |
| 473.26 | 468.30493209232344 | 4.9550679076765505 | 1.1193515979151634 |
| 470.04 | 469.5568353664925 | 0.4831646335074993 | 0.10914706208866293 |
| 472.31 | 471.0025099661929 | 1.3074900338070847 | 0.2953624624059635 |
| 467.19 | 469.9361134525985 | -2.746113452598479 | -0.6203480029931175 |
| 476.2 | 469.7928661275291 | 6.407133872470865 | 1.447373814412471 |
| 461.88 | 469.1737219130261 | -7.293721913026104 | -1.6476543672481883 |
| 471.24 | 474.2884906123142 | -3.048490612314197 | -0.688655110626592 |
| 473.02 | 468.06258267245875 | 4.957417327541236 | 1.119882332695939 |
| 471.32 | 473.485146860658 | -2.1651468606580124 | -0.489107443803926 |
| 474.23 | 472.61404029065585 | 1.6159597093441675 | 0.36504587152451 |
| 466.85 | 469.903915514171 | -3.053915514170967 | -0.6898805978802584 |
| 465.78 | 470.2469481759357 | -4.466948175935727 | -1.0090851773780105 |
| 473.46 | 467.632447347929 | 5.827552652071006 | 1.3164462111456963 |
| 466.64 | 470.9425442114299 | -4.302544211429904 | -0.9719462634817788 |
| 466.2 | 471.494284203131 | -5.294284203130985 | -1.195980679378918 |
| 467.28 | 468.30907898088435 | -1.0290789808843783 | -0.2324693067978492 |
| 475.73 | 468.87573922525866 | 6.854260774741363 | 1.5483799402320506 |
| 470.89 | 474.237754775417 | -3.347754775417002 | -0.7562589912210242 |
| 466.09 | 465.69130458295615 | 0.3986954170438253 | 9.006543612814395e-2 |
| 475.61 | 472.8180343417018 | 2.7919656582982384 | 0.6307060325245487 |
| 465.75 | 466.9269506815448 | -1.1769506815447812 | -0.2658735764273784 |
| 474.81 | 469.0391508364556 | 5.770849163544426 | 1.3036368729747179 |
| 474.26 | 468.1238036853617 | 6.136196314638312 | 1.3861689239960837 |
| 471.48 | 471.9340237695587 | -0.45402376955865975 | -0.10256413058630598 |
| 475.77 | 467.85769076077656 | 7.9123092392234184 | 1.7873934636501403 |
| 470.31 | 468.53947222591256 | 1.7705277740874408 | 0.39996285217557137 |
| 469.12 | 473.38202950699997 | -4.262029506999966 | -0.962793977380421 |
| 475.13 | 469.76472317857633 | 5.365276821423663 | 1.2120179370324367 |
| 467.41 | 468.04615604018346 | -0.6361560401834367 | -0.14370787512306984 |
| 469.83 | 469.00047164404333 | 0.8295283559566542 | 0.1873907498457296 |
| 474.46 | 474.0667420199239 | 0.39325798007610047 | 8.883711718846676e-2 |
| 472.18 | 467.138305828078 | 5.041694171922018 | 1.1389205017346977 |
| 468.46 | 468.5292032616302 | -6.920326163020718e-2 | -1.5633041348778628e-2 |
| 475.03 | 468.1969526319267 | 6.833047368073267 | 1.5435878241412961 |
| 473.49 | 469.4125049461586 | 4.077495053841403 | 0.9211075789572168 |
| 469.02 | 468.259374271566 | 0.760625728433979 | 0.17182562184847788 |
| 468.9 | 473.50603668317063 | -4.606036683170657 | -1.0405053204973118 |
| 471.19 | 468.47422142636185 | 2.715778573638147 | 0.6134953430761453 |
| 474.13 | 470.0383017110002 | 4.091698288999794 | 0.924316094817409 |
| 472.01 | 468.0562294553348 | 3.95377054466519 | 0.893158168449016 |
| 475.89 | 472.76694427363464 | 3.123055726365351 | 0.7054993963391207 |
| 471.0 | 466.0557279256278 | 4.944272074372179 | 1.1169128153424053 |
| 472.37 | 466.7407287783581 | 5.629271221641886 | 1.2716543656809969 |
| 466.08 | 464.79214736202914 | 1.2878526379708433 | 0.2909263677211387 |
| 471.61 | 470.21248865654456 | 1.3975113434554487 | 0.3156983081862408 |
| 471.44 | 472.8484021647681 | -1.4084021647681197 | -0.3181585485837661 |
| 462.3 | 470.24854120855605 | -7.948541208556037 | -1.79557827837392 |
| 461.49 | 470.2646001553115 | -8.774600155311475 | -1.9821852874504897 |
| 464.7 | 465.51076750880605 | -0.810767508806066 | -0.18315266781990724 |
| 467.68 | 466.3776971038656 | 1.3023028961343925 | 0.29419068616581573 |
| 472.41 | 466.5610830112806 | 5.848916988719452 | 1.321272422372547 |
| 468.58 | 470.92659992793443 | -2.3465999279344487 | -0.5300977560633852 |
| 472.22 | 468.98765579029424 | 3.2323442097057864 | 0.7301877034905364 |
| 469.18 | 467.5995301073336 | 1.5804698926664287 | 0.35702870934871034 |
| 473.88 | 474.44599166986796 | -0.5659916698679694 | -0.12785771898138407 |
| 461.12 | 467.5690713933053 | -6.449071393305303 | -1.4568475152442806 |
| 472.4 | 467.1768048505422 | 5.2231951494577515 | 1.1799216369387262 |
| 458.49 | 470.12758725908657 | -11.637587259086558 | -2.6289350896996853 |
| 475.36 | 469.2980914389405 | 6.061908561059511 | 1.36938729411264 |
| 473.0 | 469.1672380696391 | 3.8327619303609026 | 0.8658222795557158 |
| 461.44 | 470.1249314556345 | -8.684931455634512 | -1.961929096387823 |
| 463.9 | 471.81028761580865 | -7.91028761580867 | -1.7869367782036754 |
| 461.06 | 471.424799923348 | -10.364799923348016 | -2.3414119790965118 |
| 466.46 | 469.58969888462434 | -3.1296988846243607 | -0.7070000881461707 |
| 472.46 | 467.6133054100996 | 4.84669458990038 | 1.094870026989326 |
| 471.73 | 466.56182696263306 | 5.168173037366955 | 1.1674921223009784 |
| 471.05 | 469.14226719628124 | 1.9077328037187726 | 0.4309575170361471 |
| 467.21 | 469.69281643752356 | -2.4828164375235815 | -0.5608691138957983 |
| 472.59 | 468.5153727218602 | 4.074627278139758 | 0.9204597474090102 |
| 466.33 | 464.3095114085993 | 2.0204885914006923 | 0.45642908946816274 |
| 472.04 | 466.169002951847 | 5.870997048153015 | 1.3262603156304236 |
| 472.17 | 469.2063750462149 | 2.9636249537851427 | 0.6694839282628849 |
| 466.51 | 472.79218089209587 | -6.28218089209588 | -1.4191468918247268 |
| 460.8 | 469.8764663630868 | -9.076466363086809 | -2.0503769708593955 |
| 463.97 | 470.87346939701916 | -6.903469397019137 | -1.559496185458982 |
| 464.56 | 468.2319508045607 | -3.6719508045607085 | -0.8294949891974716 |
| 469.12 | 472.4449714286485 | -3.324971428648496 | -0.7511122251047269 |
| 471.26 | 467.1630433917525 | 4.096956608247467 | 0.9255039510005931 |
| 466.06 | 469.3130021437414 | -3.253002143741412 | -0.7348543381165886 |
| 470.66 | 470.0399558829108 | 0.6200441170892077 | 0.14006818598115695 |
| 463.65 | 464.0442884425802 | -0.3942884425802049 | -8.906989903365076e-2 |
| 470.35 | 467.7114787859095 | 2.638521214090531 | 0.5960428781510217 |
| 465.92 | 470.46126451393184 | -4.5412645139318215 | -1.02587326449146 |
| 466.67 | 467.51203531407947 | -0.8420353140794532 | -0.19021607612190924 |
| 471.38 | 469.4046202019984 | 1.9753797980015975 | 0.4462389970391413 |
| 468.45 | 470.40326389642064 | -1.9532638964206512 | -0.4412430070274576 |
| 467.22 | 469.66353144520883 | -2.443531445208805 | -0.5519946201974366 |
| 462.88 | 465.52654943877593 | -2.646549438775935 | -0.5978564569550795 |
| 473.56 | 467.6786715108734 | 5.881328489126588 | 1.3285941918109334 |
| 469.81 | 465.64964430715304 | 4.160355692846963 | 0.9398258266993826 |
| 468.66 | 467.8607823790049 | 0.7992176209951367 | 0.18054354406665346 |
| 468.35 | 467.26426723260613 | 1.0857327673938926 | 0.24526741726554308 |
| 469.94 | 467.0983914780623 | 2.841608521937701 | 0.6419203730291116 |
| 463.49 | 467.7236799517389 | -4.233679951738907 | -0.9563898027913122 |
| 465.48 | 467.5049711098421 | -2.024971109842056 | -0.4574416919740238 |
| 464.14 | 466.31353063126903 | -2.1735306312690454 | -0.49100134055869604 |
| 471.16 | 467.46352561567426 | 3.696474384325768 | 0.8350348745649541 |
| 472.67 | 467.6987016384138 | 4.971298361586207 | 1.1230180632103244 |
| 468.88 | 470.13332337428324 | -1.2533233742832408 | -0.2831261948065207 |
| 464.79 | 466.32771593378925 | -1.5377159337892294 | -0.34737057487344897 |
| 461.18 | 461.3756491943329 | -0.19564919433287287 | -4.4197222396898606e-2 |
| 468.82 | 467.03627043956425 | 1.783729560435745 | 0.4029451403943494 |
| 461.96 | 465.6730488393365 | -3.7130488393365226 | -0.8387790498308709 |
| 462.81 | 466.20636936169376 | -3.3963693616937576 | -0.76724104350462 |
| 465.62 | 471.8419294419814 | -6.221929441981388 | -1.405536067872571 |
| 464.79 | 467.025423832653 | -2.235423832652998 | -0.5049830367049549 |
| 465.39 | 467.36024219232854 | -1.9702421923285556 | -0.4450784100952837 |
| 465.51 | 470.36369995609516 | -4.85369995609517 | -1.0964525416975441 |
| 465.25 | 466.34250670048556 | -1.0925067004855578 | -0.24679765115367583 |
| 469.75 | 466.9286675356811 | 2.821332464318914 | 0.6373400044210754 |
| 468.64 | 466.5450197688411 | 2.0949802311588996 | 0.47325677731185334 |
| 456.71 | 469.52890927618625 | -12.818909276186275 | -2.895796152379444 |
| 468.87 | 464.17371789096717 | 4.696282109032836 | 1.0608917942097844 |
| 463.77 | 466.22172115408836 | -2.451721154088375 | -0.5538446783382351 |
| 464.16 | 468.9140947361635 | -4.754094736163495 | -1.0739516871848376 |
| 472.42 | 469.50273867747836 | 2.9172613225216537 | 0.6590103675152038 |
| 466.12 | 465.2396919188332 | 0.8803080811667883 | 0.19886190778235394 |
| 465.89 | 469.291500068156 | -3.401500068155997 | -0.7684000719143101 |
| 463.3 | 467.5233892738298 | -4.2233892738298096 | -0.9540651350015262 |
| 472.32 | 468.6339203654876 | 3.686079634512396 | 0.8326866968950778 |
| 472.88 | 468.23518412428797 | 4.644815875712027 | 1.049265553847453 |
| 472.52 | 467.40072495010907 | 5.119275049890916 | 1.1564460495859417 |
| 466.2 | 470.2308690130346 | -4.030869013034589 | -0.9105747398006214 |
| 464.32 | 470.06934793478035 | -5.74934793478036 | -1.298779737770393 |
| 471.52 | 468.2061275247934 | 3.313872475206608 | 0.7486049675853867 |
| 471.05 | 466.5674959516581 | 4.482504048341923 | 1.0125992544722646 |
| 465.0 | 467.4457105564226 | -2.4457105564226254 | -0.552486882193573 |
| 468.51 | 468.3292283291916 | 0.18077167080838308 | 4.083638455563342e-2 |
| 466.2 | 466.52892029567596 | -0.3289202956759709 | -7.43032114617912e-2 |
| 466.67 | 464.97984688167367 | 1.69015311832635 | 0.38180618893011364 |
| 458.19 | 469.62052738975217 | -11.430527389752172 | -2.5821601917725587 |
| 466.75 | 466.9234567199092 | -0.1734567199092112 | -3.9183934552884936e-2 |
| 467.83 | 471.0002382734735 | -3.170238273473501 | -0.7161579504665817 |
| 463.54 | 468.4888161194508 | -4.9488161194507825 | -1.1179393167374423 |
| 467.21 | 466.1266303832576 | 1.0833696167424023 | 0.24473358069515785 |
| 468.92 | 467.25704554531416 | 1.6629544546858597 | 0.37566200116631454 |
| 466.17 | 469.9193063400854 | -3.749306340085411 | -0.8469696321106422 |
| 473.56 | 469.33864000185486 | 4.221359998145147 | 0.9536067209045733 |
| 462.54 | 462.5353944778779 | 4.60552212211951e-3 | 1.040389080973342e-3 |
| 460.42 | 469.0357845125853 | -8.615784512585265 | -1.9463087774264596 |
| 463.1 | 465.4074674853422 | -2.3074674853421584 | -0.5212577234014426 |
| 469.35 | 469.2340241993221 | 0.11597580067791569 | 2.6198974509955283e-2 |
| 464.5 | 465.48772540492894 | -0.9877254049289377 | -0.22312751932133126 |
| 467.01 | 468.6698381136833 | -1.6598381136832927 | -0.3749580186284632 |
| 470.13 | 466.5182432985413 | 3.6117567014587166 | 0.8158971199557807 |
| 467.25 | 464.80335793821706 | 2.4466420617829385 | 0.5526973095848743 |
| 464.6 | 469.0300144538734 | -4.430014453873355 | -1.0007418364636667 |
| 464.56 | 468.0542535304745 | -3.4942535304745093 | -0.7893531119518733 |
| 470.44 | 466.2407858068176 | 4.199214193182399 | 0.948603975708334 |
| 464.23 | 466.68020136327283 | -2.450201363272811 | -0.5535013570539268 |
| 470.8 | 468.07562484757494 | 2.7243751524250683 | 0.615437313273332 |
| 470.19 | 468.4620083288529 | 1.7279916711470946 | 0.390353931433719 |
| 465.45 | 465.35539799683 | 9.46020031699959e-2 | 2.1370626071593804e-2 |
| 465.14 | 465.34301144661265 | -0.20301144661266335 | -4.58603577983067e-2 |
| 457.21 | 465.4373435562456 | -8.227343556245614 | -1.8585598276086635 |
| 462.69 | 470.2899851111997 | -7.5999851111997145 | -1.7168393323476852 |
| 460.66 | 465.705988879045 | -5.045988879044955 | -1.1398906775971094 |
| 462.64 | 464.775180629532 | -2.1351806295319875 | -0.4823380615634837 |
| 475.98 | 471.7241650123044 | 4.255834987695607 | 0.96139463327236 |
| 469.61 | 470.86762962520095 | -1.2576296252009342 | -0.28409897841626214 |
| 464.95 | 470.0085068177468 | -5.0585068177468315 | -1.1427184843899973 |
| 467.18 | 465.090966572103 | 2.089033427896993 | 0.47191339234564744 |
| 463.6 | 467.14100725665213 | -3.541007256652108 | -0.7999147952790192 |
| 462.77 | 465.7842034756254 | -3.014203475625436 | -0.6809096342868951 |
| 464.93 | 467.49881987016624 | -2.5688198701662373 | -0.5802973198353383 |
| 465.49 | 466.18608052784475 | -0.6960805278447424 | -0.15724483814107668 |
| 465.82 | 465.9570356485115 | -0.13703564851152805 | -3.095640160553223e-2 |
| 461.52 | 465.6600911572598 | -4.140091157259803 | -0.935248060922378 |
| 460.54 | 461.72665249570616 | -1.1866524957061415 | -0.2680652196876797 |
| 460.54 | 466.6642858393167 | -6.124285839316656 | -1.3834783433962559 |
| 463.22 | 462.1388114830899 | 1.0811885169101174 | 0.24424086946940488 |
| 467.32 | 467.1954080636746 | 0.12459193632537335 | 2.8145362608873965e-2 |
| 467.66 | 467.57038570428426 | 8.961429571576218e-2 | 2.0243901188534075e-2 |
| 470.71 | 466.8779893764293 | 3.8320106235706817 | 0.8656525590853151 |
| 466.34 | 468.28371557585353 | -1.9437155758535596 | -0.43908603802454604 |
| 464.0 | 465.49312878230023 | -1.4931287823002322 | -0.3372983215369459 |
| 454.16 | 460.9202947940051 | -6.760294794005063 | -1.527152991853827 |
| 460.19 | 464.1520666190232 | -3.962066619023176 | -0.8950322545891167 |
| 467.71 | 465.5258417359535 | 2.1841582640464594 | 0.49340212657275334 |
| 465.01 | 464.28156360850716 | 0.7284363914928349 | 0.16455403921586076 |
| 462.87 | 464.1635216732549 | -1.2935216732548724 | -0.2922070048026174 |
| 466.73 | 466.3736543730528 | 0.35634562694724536 | 8.049860352378384e-2 |
| 469.34 | 467.08552274770574 | 2.2544772522942367 | 0.5092872109601911 |
| 458.99 | 465.25377872135545 | -6.263778721355436 | -1.4149898349271792 |
| 465.72 | 464.56900508273293 | 1.1509949172670986 | 0.26001016007049604 |
| 465.63 | 461.87533959682145 | 3.7546604031785478 | 0.848179116862477 |
| 471.24 | 464.4149735638803 | 6.8250264361196855 | 1.541775892767944 |
| 456.41 | 468.5317634787664 | -12.121763478766354 | -2.7383106694634813 |
| 460.51 | 466.42415027580006 | -5.914150275800068 | -1.3360086450624162 |
| 467.77 | 466.6719213555882 | 1.0980786444117712 | 0.24805635526298545 |
| 456.63 | 464.60786745115865 | -7.977867451158659 | -1.8022030869799206 |
| 468.02 | 467.544960954765 | 0.4750390452350075 | 0.10731148881574702 |
| 463.93 | 466.26419991369926 | -2.3341999136992513 | -0.5272965884493478 |
| 463.12 | 465.43441550001705 | -2.3144155000170485 | -0.522827282380973 |
| 460.06 | 467.4928608009505 | -7.432860800950493 | -1.6790858913831084 |
| 461.6 | 466.41675498345813 | -4.816754983458111 | -1.0881066592743878 |
| 463.27 | 466.61302775183003 | -3.34302775183005 | -0.7551911549160485 |
| 467.82 | 462.99098636012917 | 4.829013639870823 | 1.0908758941061858 |
| 462.59 | 464.3870766666383 | -1.7970766666383042 | -0.40596025641974887 |
| 460.15 | 465.9823776729848 | -5.832377672984819 | -1.3175361850815699 |
| 463.1 | 464.5934173732165 | -1.4934173732164595 | -0.33736351432728534 |
| 461.6 | 464.9855482511964 | -3.385548251196383 | -0.7647965508049966 |
| 462.64 | 466.6093718548404 | -3.9693718548404036 | -0.8966825099514081 |
| 460.56 | 459.9584434481331 | 0.6015565518669064 | 0.13589183843986818 |
| 459.85 | 461.87302187524506 | -2.0230218752450355 | -0.45700135918716 |
| 465.99 | 465.2019993258076 | 0.7880006741924035 | 0.17800963180524587 |
| 464.49 | 465.03190605144545 | -0.5419060514454372 | -0.12241676923652416 |
| 468.62 | 464.51031407803373 | 4.109685921966275 | 0.928379507484781 |
| 458.26 | 464.7510595901315 | -6.491059590131499 | -1.4663326638006033 |
| 466.5 | 462.10563966655616 | 4.394360333443842 | 0.9926875580120624 |
| 459.31 | 463.52420449023833 | -4.21420449023833 | -0.9519902891303528 |
| 459.77 | 465.12795484714036 | -5.357954847140377 | -1.2103639004447255 |
| 457.1 | 458.39794118753656 | -1.2979411875365372 | -0.29320537464645474 |
| 469.18 | 464.49240158564226 | 4.687598414357751 | 1.0589301445025614 |
| 472.28 | 463.4938095964465 | 8.786190403553462 | 1.9848035286394352 |
| 459.15 | 464.74092024201923 | -5.590920242019251 | -1.262990865781086 |
| 466.63 | 468.69706628494185 | -2.067066284941859 | -0.4669510069602996 |
| 462.69 | 464.01147313398934 | -1.3214731339893433 | -0.29852124969696514 |
| 468.53 | 466.52540885533836 | 2.0045911446616174 | 0.4528378506109543 |
| 453.99 | 463.6262300043777 | -9.636230004377694 | -2.176827775976124 |
| 471.97 | 469.18664060318713 | 2.783359396812898 | 0.6287618750023547 |
| 457.74 | 458.7253721171194 | -0.9853721171193683 | -0.22259591077043436 |
| 466.83 | 462.85471321992253 | 3.975286780077454 | 0.8980186936604918 |
| 466.2 | 469.16650047432273 | -2.9665004743227428 | -0.6701335093722809 |
| 465.14 | 464.90298984127037 | 0.23701015872961761 | 5.3540678924927795e-2 |
| 457.36 | 466.2852656032666 | -8.92526560326661 | -2.0162206655848456 |
| 439.21 | 465.39633546049805 | -26.18633546049807 | -5.915502468864325 |
| 462.59 | 462.31339644999167 | 0.2766035500083035 | 6.248484005862629e-2 |
| 457.3 | 457.58467899039783 | -0.28467899039782196 | -6.430908490699098e-2 |
| 463.57 | 462.46440155189674 | 1.105598448103251 | 0.24975508158417795 |
| 462.09 | 461.6436673101792 | 0.44633268982079244 | 0.10082671294548266 |
| 465.41 | 465.13586499514474 | 0.27413500485528175 | 6.192719483296151e-2 |
| 464.17 | 468.0777122780922 | -3.9077122780921627 | -0.8827535896932235 |
| 464.59 | 463.94240853336476 | 0.6475914666352196 | 0.14629114201469284 |
| 464.38 | 462.7655254273002 | 1.6144745726998053 | 0.3647103786297681 |
| 464.95 | 464.3512532840681 | 0.5987467159318953 | 0.13525709550549558 |
| 455.15 | 458.1774466179169 | -3.027446617916951 | -0.6839012648279135 |
| 456.91 | 458.36321179088014 | -1.4532117908801183 | -0.3282810590165173 |
| 456.21 | 467.4754642409999 | -11.265464240999904 | -2.5448723679212413 |
| 467.3 | 462.93565136830296 | 4.364348631697055 | 0.9859079039422615 |
| 464.72 | 460.79339608207107 | 3.926603917928958 | 0.8870212178332524 |
| 462.58 | 466.71318747004864 | -4.133187470048654 | -0.9336885155326297 |
| 466.6 | 469.2173130099923 | -2.6173130099923014 | -0.5912519373226456 |
| 459.42 | 464.720482732063 | -5.30048273206296 | -1.19738092926328 |
| 464.14 | 464.335595847906 | -0.19559584790602003 | -4.418517142015048e-2 |
| 460.45 | 465.0880608446689 | -4.638060844668928 | -1.0477395899387958 |
| 468.91 | 465.0497057218854 | 3.8602942781146226 | 0.8720418466790518 |
| 466.39 | 467.13452750857033 | -0.7445275085703429 | -0.16818902827121646 |
| 467.22 | 463.63133669846115 | 3.5886633015388725 | 0.8106803127226195 |
| 462.77 | 462.43127985662505 | 0.3387201433749283 | 7.651700053300835e-2 |
| 464.95 | 462.0817954663473 | 2.8682045336527153 | 0.6479284215091841 |
| 457.12 | 462.7228887148107 | -5.602888714810717 | -1.2656945480299158 |
| 461.5 | 463.2389898882613 | -1.738989888261301 | -0.3928384325809046 |
| 458.46 | 465.7322155460913 | -7.272215546091331 | -1.6427960713292782 |
| 465.89 | 465.9391561803266 | -4.91561803266336e-2 | -1.1104398571567456e-2 |
| 466.15 | 464.67588000342283 | 1.4741199965771443 | 0.33300423009965163 |
| 455.48 | 462.03627301808893 | -6.556273018088916 | -1.4810644003074978 |
| 464.95 | 468.46315966006046 | -3.5131596600604666 | -0.7936240133313774 |
| 454.88 | 456.7218449478403 | -1.8418449478402863 | -0.41607342702266226 |
| 457.41 | 460.9973530979208 | -3.5873530979207544 | -0.8103843372605553 |
| 468.88 | 468.60583110746245 | 0.2741688925375456 | 6.1934850072404936e-2 |
| 461.86 | 468.0452621538944 | -6.185262153894371 | -1.3972529144877643 |
| 456.08 | 461.16748971043216 | -5.08748971043218 | -1.1492657301278992 |
| 462.94 | 462.09664222758136 | 0.8433577724186421 | 0.19051481992981023 |
| 460.87 | 460.8634611992421 | 6.538800757880381e-3 | 1.4771174105289492e-3 |
| 461.06 | 464.39198726436115 | -3.331987264361146 | -0.7526971048807434 |
| 454.94 | 458.02055270119473 | -3.0805527011947333 | -0.6958979478771827 |
| 466.22 | 464.5123542802937 | 1.7076457197063064 | 0.3857577737865013 |
| 459.95 | 461.1346442030387 | -1.1846442030387152 | -0.26761154566176326 |
| 463.47 | 463.18977819659995 | 0.2802218034000816 | 6.330220478322962e-2 |
| 456.58 | 464.6775725663142 | -8.097572566314227 | -1.8292445149531718 |
| 463.57 | 465.5402356742791 | -1.9702356742790812 | -0.4450769376655955 |
| 462.44 | 462.1000323229214 | 0.33996767707861864 | 7.679881883917433e-2 |
| 448.59 | 459.28384302135794 | -10.693843021357964 | -2.4157429316491066 |
| 462.5 | 464.0520812837949 | -1.5520812837949052 | -0.35061571253514523 |
| 463.76 | 462.0021066209579 | 1.757893379042116 | 0.39710873785339107 |
| 459.48 | 464.6824431291315 | -5.202443129131495 | -1.1752337481862478 |
| 455.05 | 457.21309738475657 | -2.163097384756554 | -0.488644466470834 |
| 456.11 | 461.3420214113817 | -5.232021411381709 | -1.1819154926380382 |
| 472.17 | 467.91529134370023 | 4.254708656299783 | 0.9611401946105317 |
| 461.54 | 465.4513233379416 | -3.9113233379415533 | -0.88356932939405 |
| 459.69 | 461.3200136826322 | -1.6300136826321818 | -0.3682206690752407 |
| 461.54 | 459.19347653551756 | 2.346523464482459 | 0.5300804829424589 |
| 462.48 | 459.1522349051031 | 3.327765094896904 | 0.7517433153011605 |
| 442.48 | 460.6299621913311 | -18.1499621913311 | -4.100082896844213 |
| 465.14 | 465.94867946942674 | -0.8086794694267496 | -0.18268097898348856 |
| 464.62 | 462.5145658398506 | 2.1054341601494 | 0.4756183235788339 |
| 465.78 | 463.3496922983309 | 2.430307701669051 | 0.5490073718413218 |
| 462.52 | 461.99087118644087 | 0.5291288135591117 | 0.11953038666589516 |
| 454.78 | 457.1797966088055 | -2.3997966088055023 | -0.5421149051411095 |
| 465.03 | 464.9190479188059 | 0.11095208119405697 | 2.5064114496629736e-2 |
| 463.74 | 462.72099107520137 | 1.0190089247986407 | 0.230194477556215 |
| 459.59 | 461.9941154580848 | -2.4041154580848456 | -0.5430905347252163 |
| 465.6 | 458.7011562788462 | 6.89884372115381 | 1.5584512436402695 |
| 464.7 | 461.4647200415145 | 3.235279958485478 | 0.7308508901811838 |
| 460.54 | 462.40970062782714 | -1.8697006278271147 | -0.4223660350121439 |
| 465.52 | 462.47440172010425 | 3.045598279895728 | 0.6880017317073447 |
| 465.64 | 464.23959443772316 | 1.4004055622768306 | 0.3163521132445753 |
| 468.22 | 468.12040219816635 | 9.959780183368139e-2 | 2.2499178761738656e-2 |
| 464.2 | 461.97170222353844 | 2.2282977764615453 | 0.5033732580836815 |
| 472.63 | 464.18342103476823 | 8.446578965231765 | 1.908085184261808 |
| 460.31 | 460.4941449517029 | -0.18414495170287637 | -4.159840990620793e-2 |
| 458.25 | 461.5472235985969 | -3.2972235985968723 | -0.7448439804538625 |
| 459.25 | 462.26646301676067 | -3.0164630167606674 | -0.681420065430807 |
| 463.62 | 462.2293585659873 | 1.3906414340127071 | 0.3141463932063569 |
| 457.26 | 464.22591401634116 | -6.9659140163411735 | -1.573602447113043 |
| 460.0 | 459.5632798612247 | 0.4367201387752857 | 9.865523425471445e-2 |
| 460.25 | 461.3519558174483 | -1.1019558174482995 | -0.24893221002717708 |
| 464.6 | 460.4994805562497 | 4.100519443750329 | 0.9263087968044336 |
| 461.49 | 463.40687222781077 | -1.9168722278107566 | -0.43302211618029557 |
| 449.98 | 456.35386691539543 | -6.373866915395411 | -1.4398587970092795 |
| 464.72 | 462.67093183857946 | 2.049068161420564 | 0.462885224472874 |
| 460.08 | 461.81109623075827 | -1.7310962307582827 | -0.391055252551079 |
| 454.19 | 460.0644340540906 | -5.874434054090614 | -1.3270367364908389 |
| 464.27 | 462.014274652855 | 2.25572534714496 | 0.5095691560296709 |
| 456.55 | 460.5836092644097 | -4.033609264409677 | -0.9111937635582255 |
| 464.5 | 463.360199769915 | 1.1398002300849726 | 0.25748127626527917 |
| 468.8 | 464.34868588625807 | 4.451314113741944 | 1.0055534371830066 |
| 465.16 | 461.6467454037935 | 3.5132545962065365 | 0.7936454594404486 |
| 463.65 | 462.4960995942487 | 1.1539004057513012 | 0.26066651094965915 |
| 466.36 | 460.43082906265056 | 5.929170937349454 | 1.3394018178342768 |
| 455.53 | 462.1757595182412 | -6.645759518241221 | -1.5012794324329175 |
| 454.06 | 459.4288341311474 | -5.368834131147423 | -1.212821534560818 |
| 453.0 | 454.3258801823864 | -1.3258801823864133 | -0.2995168034930508 |
| 461.47 | 460.83972369537236 | 0.6302763046276709 | 0.14237964077547013 |
| 457.67 | 459.652078633235 | -1.9820786332350053 | -0.44775226629445647 |
| 458.67 | 457.84281119185783 | 0.8271888081421821 | 0.18686224516459876 |
| 461.01 | 462.29977029786545 | -1.2897702978654593 | -0.2913595677715297 |
| 458.34 | 458.99629100134234 | -0.6562910013423675 | -0.14825636999077735 |
| 457.01 | 456.94689658887796 | 6.310341112202877e-2 | 1.4255082955353757e-2 |
| 455.75 | 455.92052796835753 | -0.1705279683575327 | -3.85223285385276e-2 |
| 456.62 | 455.0999707958263 | 1.5200292041736816 | 0.34337513637978356 |
| 457.67 | 463.6885973525347 | -6.018597352534698 | -1.3596032767443251 |
| 456.16 | 456.72206228080466 | -0.5620622808046392 | -0.12697006859821677 |
| 455.97 | 460.34419741616995 | -4.37419741616992 | -0.9881327478480624 |
| 457.05 | 457.5432003190509 | -0.49320031905091355 | -0.11141412701259497 |
| 457.71 | 456.69285780084897 | 1.0171421991510101 | 0.22977278357028447 |
| 459.43 | 457.13828420727975 | 2.291715792720254 | 0.5176994104510022 |
| 457.98 | 457.7082041695146 | 0.27179583048541645 | 6.13987745130998e-2 |
| 459.13 | 460.61769921749 | -1.4876992174899897 | -0.3360717809204362 |
| 457.35 | 460.2397779497155 | -2.8897779497154943 | -0.6528018638498932 |
| 456.56 | 454.03599822567077 | 2.5240017743292356 | 0.5701728960887047 |
| 454.57 | 457.06546864918414 | -2.495468649184147 | -0.5637272529976811 |
| 451.9 | 457.7462919446442 | -5.846291944644236 | -1.3206794239848478 |
| 457.33 | 456.68265807481106 | 0.6473419251889254 | 0.1462347705134622 |
| 467.59 | 461.5754309315541 | 6.014569068445894 | 1.358693285939825 |
| 463.99 | 464.77705443177615 | -0.7870544317761414 | -0.17779587530777846 |
| 452.55 | 459.8014970847607 | -7.251497084760672 | -1.6381157635658086 |
| 452.28 | 454.94641693932414 | -2.666416939324165 | -0.602344532375961 |
| 453.55 | 454.8958623057626 | -1.34586230576258 | -0.3040307722514201 |
| 448.88 | 452.5071708494883 | -3.627170849488323 | -0.8193791814630933 |
| 448.71 | 455.24182428555105 | -6.5318242855510675 | -1.4755414229551909 |
| 454.59 | 459.58273190303845 | -4.992731903038475 | -1.1278599078269054 |
| 451.14 | 453.10756062981807 | -1.967560629818081 | -0.4444726441730127 |
| 458.01 | 456.42171751548034 | 1.5882824845196524 | 0.35879357661949324 |
| 456.55 | 456.5005129659639 | 4.948703403613308e-2 | 1.1179138541897764e-2 |
| 458.04 | 456.4920988999565 | 1.547901100043532 | 0.34967140754299175 |
| 463.31 | 463.61908166044077 | -0.3090816604407678 | -6.982165672536109e-2 |
| 454.34 | 454.4243094498896 | -8.430944988964484e-2 | -1.904553463477631e-2 |
| 454.29 | 454.14166454199494 | 0.14833545800507864 | 3.3509032578186985e-2 |
| 455.82 | 453.8961915229473 | 1.9238084770527166 | 0.43458901734435373 |
| 451.44 | 453.6649941349345 | -2.224994134934491 | -0.5026269642909126 |
| 442.45 | 449.9074433355401 | -7.45744333554012 | -1.684639094666372 |
| 454.98 | 459.22851589459475 | -4.248515894594732 | -0.9597412475447774 |
| 465.26 | 459.05202239898534 | 6.2079776010146475 | 1.4023843420494426 |
| 451.06 | 452.94903640134396 | -1.889036401343958 | -0.4267339931080307 |
| 452.77 | 453.1802042498492 | -0.41020424984924375 | -9.266528554109059e-2 |
| 463.47 | 461.3678096135659 | 2.102190386434131 | 0.47488555394598253 |
| 461.73 | 460.89674705500823 | 0.8332529449917843 | 0.18823213582988355 |
| 467.54 | 463.8188758742396 | 3.7211241257604115 | 0.8406032598983453 |
| 454.74 | 459.1270333631719 | -4.387033363171895 | -0.9910323928287407 |
| 451.04 | 455.213182837326 | -4.173182837325953 | -0.9427234831869965 |
| 464.13 | 457.4956830813669 | 6.63431691863309 | 1.4986945451227311 |
| 456.25 | 452.8810496638165 | 3.3689503361835023 | 0.7610470759162783 |
| 457.43 | 456.48090497858726 | 0.9490950214127452 | 0.21440090198276052 |
| 448.17 | 456.5591352486571 | -8.389135248657112 | -1.8951086283122098 |
| 452.93 | 453.45921998591575 | -0.5292199859157449 | -0.1195509825335976 |
| 455.24 | 454.7581350474279 | 0.4818649525720957 | 0.10885346370435622 |
| 454.44 | 461.43742015467615 | -6.997420154676149 | -1.580719694938195 |
| 460.27 | 463.53345887643894 | -3.2634588764389605 | -0.7372165177419857 |
| 450.5 | 452.02894677235804 | -1.52894677235804 | -0.3453896181957068 |
| 455.22 | 455.963340998892 | -0.7433409988919948 | -0.1679209953140206 |
| 457.17 | 456.69115708185933 | 0.47884291814068547 | 0.10817078505437988 |
| 458.47 | 455.70816977608035 | 2.7618302239196737 | 0.623898427209341 |
| 456.49 | 455.5784197907349 | 0.9115802092650824 | 0.2059262926120357 |
| 451.29 | 457.8698842565306 | -6.57988425653059 | -1.4863981874464185 |
| 453.78 | 454.0602820488982 | -0.2802820488982434 | -6.331581426263354e-2 |
| 463.2 | 460.7479119618785 | 2.452088038121474 | 0.5539275575714465 |
| 452.1 | 453.92151217696727 | -1.8215121769672464 | -0.4114802468703741 |
| 451.93 | 453.0532072948905 | -1.1232072948905056 | -0.2537329263193067 |
| 450.55 | 454.758298968509 | -4.208298968508984 | -0.9506562296769948 |
| 461.49 | 456.7605922899199 | 4.729407710080125 | 1.068374879236074 |
| 452.52 | 454.0242328275001 | -1.5042328275001182 | -0.3398067292862239 |
| 451.23 | 452.6641989591518 | -1.4341989591517859 | -0.32398605358520316 |
| 455.44 | 457.32803096446935 | -1.8880309644693511 | -0.42650686456142684 |
| 440.64 | 450.3190565318654 | -9.679056531865399 | -2.186502303726239 |
| 467.51 | 461.3397464375879 | 6.170253562412086 | 1.3938624683483432 |
| 452.37 | 456.8872770659861 | -4.517277065986093 | -1.0204544914921314 |
| 462.05 | 460.8339970164602 | 1.216002983539795 | 0.27469550529997006 |
| 464.48 | 462.10615685280686 | 2.3738431471931563 | 0.5362520089571434 |
| 453.79 | 457.0494808979769 | -3.2594808979768572 | -0.7363178909964009 |
| 449.55 | 452.3356980438659 | -2.785698043865864 | -0.6292901762011418 |
| 459.45 | 455.30258286482143 | 4.147417135178557 | 0.9369030019327613 |
| 463.02 | 462.7299869900826 | 0.2900130099174021 | 6.551404180844994e-2 |
| 455.79 | 455.2223463598715 | 0.5676536401285261 | 0.1282331586527475 |
| 453.25 | 453.31727625144936 | -6.7276251449357e-2 | -1.5197729065410778e-2 |
| 454.71 | 459.55748654713716 | -4.84748654713718 | -1.0950489304100275 |
| 450.74 | 456.7379403460756 | -5.997940346075609 | -1.3549368516581415 |
| 453.9 | 456.5169354267787 | -2.6169354267786957 | -0.5911666411407626 |
| 450.87 | 451.9912034594031 | -1.121203459403091 | -0.2532802591719359 |
| 453.28 | 457.39523074964615 | -4.115230749646173 | -0.9296320860244299 |
| 454.47 | 454.91390716792046 | -0.44390716792042895 | -0.1002787866878538 |
| 446.7 | 453.70377279073705 | -7.0037727907370595 | -1.5821547576776454 |
| 454.58 | 458.80810089986693 | -4.228100899866945 | -0.9551294929945369 |
| 458.64 | 459.2317295422404 | -0.5917295422403868 | -0.13367191347958032 |
| 454.02 | 452.8313052555021 | 1.188694744497866 | 0.2685265644141194 |
| 455.88 | 453.2048261109298 | 2.6751738890702086 | 0.6043227304296972 |
| 460.19 | 460.78946144208953 | -0.5994614420895346 | -0.13541855239802086 |
| 457.58 | 456.60185019595883 | 0.978149804041152 | 0.22096438768429855 |
| 459.68 | 456.60000256329795 | 3.079997436702058 | 0.6957725134313342 |
| 454.6 | 461.5337919917975 | -6.933791991797477 | -1.566346070374885 |
| 457.55 | 455.08314377928764 | 2.4668562207123728 | 0.5572636952570301 |
| 455.42 | 454.7344713102365 | 0.6855286897635438 | 0.15486117417577897 |
| 455.76 | 454.4253732327452 | 1.3346267672548038 | 0.3014926601172488 |
| 447.47 | 450.84382297953874 | -3.3738229795387156 | -0.7621478077785558 |
| 455.7 | 454.2509501800389 | 1.4490498199610897 | 0.32734086830965053 |
| 455.95 | 463.1377560912732 | -7.187756091273229 | -1.6237166505279563 |
| 451.05 | 455.0606464477159 | -4.010646447715885 | -0.9060064551221478 |
| 449.89 | 452.78710305999687 | -2.89710305999688 | -0.6544566088606873 |
| 451.49 | 452.43308379405045 | -0.9430837940504375 | -0.21304296358942265 |
| 456.04 | 453.7206916287245 | 2.319308371275497 | 0.5239325837337663 |
| 458.06 | 453.97719041615505 | 4.0828095838449485 | 0.9223081331700905 |
| 448.17 | 452.20842313451385 | -4.038423134513835 | -0.9122812185222428 |
| 451.8 | 452.90074763749135 | -1.100747637491338 | -0.24865928174635601 |
| 465.66 | 461.8378158906793 | 3.8221841093207445 | 0.8634327460307729 |
| 449.26 | 452.41543202652065 | -3.155432026520657 | -0.7128132140281332 |
| 451.08 | 457.75864371455697 | -6.6786437145569835 | -1.5087079840447288 |
| 467.72 | 461.3187533462041 | 6.4012466537959085 | 1.4460438896255918 |
| 463.35 | 462.37131638295244 | 0.9786836170475794 | 0.22108497623179488 |
| 453.47 | 454.29376940501464 | -0.823769405014616 | -0.18608980078521345 |
| 450.88 | 453.15381920341724 | -2.273819203417247 | -0.5136565645795023 |
| 456.08 | 457.3375291656138 | -1.2575291656137892 | -0.2840762845598929 |
| 448.04 | 451.78495851322157 | -3.7449585132215475 | -0.8459874564799138 |
| 450.17 | 451.8524216632198 | -1.6824216632197704 | -0.380059650479177 |
| 450.54 | 455.69553323527913 | -5.155533235279108 | -1.1646367865259744 |
| 456.61 | 453.9716415466405 | 2.6383584533595013 | 0.5960061104441506 |
| 448.73 | 447.41632457686086 | 1.3136754231391592 | 0.29675974405004724 |
| 456.57 | 456.66826631159057 | -9.826631159057797e-2 | -2.2198394643539735e-2 |
| 456.06 | 451.3868160236597 | 4.673183976340283 | 1.0556739178415897 |
| 457.41 | 455.8596185860852 | 1.5503814139148062 | 0.35023171132628417 |
| 446.29 | 451.60844251280884 | -5.318442512808815 | -1.2014380501041335 |
| 453.84 | 451.542577765937 | 2.297422234062992 | 0.5189884975744112 |
| 456.03 | 454.3347436794228 | 1.6952563205771867 | 0.38295900412869216 |
| 461.16 | 456.98501253896984 | 4.1749874610301845 | 0.9431311483218855 |
| 450.5 | 450.35966629930124 | 0.14033370069876128 | 3.170143276445295e-2 |
| 459.12 | 453.2290277149437 | 5.890972285056307 | 1.3307727287321278 |
| 444.87 | 450.8892362675085 | -6.019236267508518 | -1.3597476078635353 |
| 468.27 | 460.77739524450277 | 7.4926047554972115 | 1.6925820719064946 |
| 452.04 | 451.5662781980391 | 0.4737218019609486 | 0.10701392309290869 |
| 456.89 | 455.22151625901614 | 1.668483740983845 | 0.3769110689022767 |
| 456.55 | 458.86327155628703 | -2.3132715562870203 | -0.5225688650866008 |
| 455.07 | 454.4962025118092 | 0.5737974881907917 | 0.12962105610924668 |
| 454.94 | 452.9973261711798 | 1.9426738288202046 | 0.4388507070002017 |
| 444.64 | 451.56134671760844 | -6.921346717608458 | -1.5635346785212256 |
| 464.54 | 461.26403285215525 | 3.275967147844767 | 0.7400421406892627 |
| 454.28 | 454.96273138933435 | -0.6827313893343785 | -0.1542292630166233 |
| 450.26 | 450.45712572408434 | -0.19712572408434426 | -4.453077099148361e-2 |
| 450.44 | 452.7340333117575 | -2.2940333117575165 | -0.5182229388235478 |
| 449.23 | 451.3669335966618 | -2.136933596661777 | -0.48273405745988357 |
| 452.41 | 450.86300710254875 | 1.5469928974512754 | 0.34946624425526 |
| 451.75 | 450.87541624186673 | 0.8745837581332694 | 0.1975687811786549 |
| 449.66 | 451.1697942873412 | -1.5097942873411512 | -0.34106306503697253 |
| 446.03 | 450.6475445784032 | -4.617544578403226 | -1.0431049581122194 |
| 456.36 | 453.93684539976664 | 2.4231546002333744 | 0.5473914836897852 |
| 451.22 | 452.6438110022995 | -1.4238110022994874 | -0.32163941044764294 |
| 440.03 | 449.38085095551605 | -9.35085095551608 | -2.1123605476138505 |
| 462.86 | 459.86949886435633 | 2.990501135643683 | 0.6755552672777567 |
| 451.14 | 454.5790164502554 | -3.4390164502553944 | -0.7768750359376887 |
| 456.03 | 455.7041227162542 | 0.32587728374579683 | 7.361579398740335e-2 |
| 446.35 | 451.5591661954991 | -5.209166195499051 | -1.1767524912633618 |
| 448.85 | 451.4495789708702 | -2.5995789708701977 | -0.5872458115946685 |
| 448.71 | 449.41092940189 | -0.7009294018899936 | -0.15834020050780284 |
| 459.39 | 456.368436088565 | 3.0215639114350097 | 0.682572359347037 |
| 456.53 | 455.3814815227917 | 1.1485184772082562 | 0.25945073138280605 |
| 455.58 | 454.77441817350825 | 0.805581826491732 | 0.18198122034572886 |
| 459.47 | 455.56852272126105 | 3.90147727873898 | 0.8813451011277746 |
| 453.8 | 453.3887778929569 | 0.4112221070431019 | 9.289521984221884e-2 |
| 447.1 | 450.7408294300028 | -3.640829430002782 | -0.8224646596460314 |
| 446.57 | 451.436105216529 | -4.866105216528979 | -1.0992548944297087 |
| 455.28 | 454.1835978057384 | 1.0964021942616 | 0.24767764457942207 |
| 453.96 | 461.1739549532308 | -7.213954953230825 | -1.6296349827369583 |
| 454.5 | 450.58677338372456 | 3.9132266162754377 | 0.8839992806447705 |
| 449.31 | 450.2319334353776 | -0.9219334353775821 | -0.20826509006315633 |
| 449.63 | 454.6358406173096 | -5.005840617309616 | -1.1308211710304394 |
| 448.92 | 452.85108866192866 | -3.9310886619286407 | -0.888034323093527 |
| 457.14 | 451.05259673451775 | 6.087403265482237 | 1.375146556884749 |
| 450.98 | 451.5232844413466 | -0.5432844413466 | -0.12272814800411408 |
| 452.67 | 450.58585768893414 | 2.084142311065875 | 0.4708084873186104 |
| 449.03 | 451.0815006280822 | -2.0515006280822377 | -0.4634347195545461 |
| 453.33 | 451.80697350389795 | 1.5230264961020339 | 0.3440522256895429 |
| 455.13 | 453.19662660647845 | 1.9333733935215491 | 0.4367497353673552 |
| 454.36 | 455.59739505823774 | -1.2373950582377233 | -0.2795279825620374 |
| 454.84 | 451.430922332517 | 3.4090776674829613 | 0.7701118542900588 |
| 454.23 | 454.4162557726164 | -0.18625577261639137 | -4.2075245099282746e-2 |
| 447.06 | 447.51848167260005 | -0.458481672600044 | -0.10357117246458049 |
| 457.57 | 457.4899833309971 | 8.001666900287319e-2 | 1.80757939097979e-2 |
| 455.49 | 454.7734968589603 | 0.7165031410397091 | 0.16185831370575537 |
| 462.44 | 459.9896954813451 | 2.450304518654889 | 0.5535246598913276 |
| 451.59 | 453.5274885789799 | -1.9374885789799237 | -0.4376793572220653 |
| 452.45 | 451.13958592197287 | 1.3104140780271223 | 0.2960230050324334 |
| 448.79 | 451.01129177115814 | -2.2212917711581213 | -0.501790599000596 |
| 450.25 | 452.16097295111786 | -1.9109729511178557 | -0.4316894674828857 |
| 456.11 | 455.0355456385039 | 1.074454361496123 | 0.24271962137276715 |
| 445.58 | 448.2416124360999 | -2.661612436099915 | -0.6012591933934375 |
| 452.96 | 450.748723139911 | 2.211276860088958 | 0.499528227037766 |
| 452.94 | 453.9480760674323 | -1.0080760674322846 | -0.227724741199252 |
| 452.39 | 459.1480198621134 | -6.758019862113429 | -1.5266390839325845 |
| 445.96 | 448.54249260358677 | -2.582492603586786 | -0.5833859951647822 |
| 452.8 | 450.84813038147 | 1.9518696185299973 | 0.44092803915740614 |
| 458.97 | 452.9523382793844 | 6.017661720615649 | 1.3593919171619417 |
| 460.1 | 457.2797775931854 | 2.8202224068146506 | 0.637089242037123 |
| 453.83 | 450.67534900317014 | 3.1546509968298437 | 0.712636779143946 |
| 443.76 | 448.7439698617689 | -4.983969861768912 | -1.1258805595962003 |
| 450.46 | 449.81767655065505 | 0.6423234493449286 | 0.1451010950402414 |
| 446.53 | 449.46273191454395 | -2.9327319145439787 | -0.6625051797404924 |
| 446.34 | 450.23276134284185 | -3.8927613428418795 | -0.8793761681170623 |
| 449.72 | 448.3011628860887 | 1.4188371139113087 | 0.3205158072965067 |
| 447.14 | 446.65132022669536 | 0.4886797733046251 | 0.11039293412506312 |
| 455.5 | 459.6412858596406 | -4.141285859640618 | -0.935517944613967 |
| 448.22 | 449.8084139802319 | -1.5884139802318487 | -0.35882328154752013 |
| 447.84 | 450.0492245621943 | -2.209224562194322 | -0.4990646122154142 |
| 447.58 | 450.1534891767275 | -2.5734891767274917 | -0.5813521178437366 |
| 447.06 | 447.2352893702614 | -0.17528937026139602 | -3.959793091748769e-2 |
| 447.69 | 452.1889854808296 | -4.498985480829617 | -1.016322415917227 |
| 462.01 | 457.2763644769643 | 4.733635523035673 | 1.069329943682345 |
| 448.92 | 449.22543586447597 | -0.30543586447595317 | -6.899807012373943e-2 |
| 442.82 | 446.9636998742384 | -4.1436998742383935 | -0.9360632713678595 |
| 453.46 | 456.1194901357003 | -2.6594901357003096 | -0.600779764980383 |
| 446.2 | 450.6991034809647 | -4.49910348096472 | -1.0163490721896018 |
| 443.77 | 447.0742816761689 | -3.3042816761688982 | -0.7464384026808953 |
| 448.9 | 447.93156732156075 | 0.9684326784392283 | 0.21876928556414296 |
| 460.6 | 459.53962876300176 | 1.0603712369982645 | 0.23953823855339262 |
| 444.44 | 450.41539217977487 | -5.975392179774872 | -1.349843212892932 |
| 445.95 | 450.2091683575852 | -4.259168357585224 | -0.9621476427127764 |
| 453.18 | 450.5270199129538 | 2.652980087046217 | 0.599309142680298 |
| 443.46 | 446.7475520504839 | -3.287552050483896 | -0.7426591743046266 |
| 444.64 | 444.5071996193093 | 0.13280038069069633 | 2.999965310254963e-2 |
| 465.57 | 459.3265106832487 | 6.2434893167512655 | 1.4104064512304106 |
| 446.41 | 447.6568115900025 | -1.246811590002494 | -0.281655180427759 |
| 450.39 | 454.7627521673882 | -4.372752167388228 | -0.9878062656356458 |
| 452.38 | 454.2185133216816 | -1.838513321681603 | -0.4153208114916822 |
| 445.66 | 445.39798853968557 | 0.2620114603144543 | 5.918848182094669e-2 |
| 444.31 | 446.5197598700026 | -2.2097598700025856 | -0.4991855384391755 |
| 450.95 | 449.2875712413884 | 1.662428758611611 | 0.37554324623667645 |
| 442.02 | 446.61697690688305 | -4.5969769068830715 | -1.0384587138204282 |
| 453.88 | 452.35263519535 | 1.5273648046500057 | 0.34503225112933517 |
| 454.15 | 453.8829146493028 | 0.26708535069718664 | 6.033467545812348e-2 |
| 455.22 | 449.0737291884585 | 6.146270811541513 | 1.388444756419958 |
| 446.44 | 446.2148995417375 | 0.2251004582624887 | 5.085027336501233e-2 |
| 449.6 | 453.8663667211949 | -4.266366721194856 | -0.9637737556054642 |
| 457.89 | 451.0124137711604 | 6.8775862288395615 | 1.553649168586412 |
| 446.02 | 448.5883814092364 | -2.5683814092363946 | -0.5801982713557641 |
| 445.4 | 449.5097304398748 | -4.109730439874795 | -0.9283895640961064 |
| 449.74 | 448.5033053489824 | 1.2366946510176149 | 0.2793697603225793 |
| 448.31 | 446.9172206057899 | 1.3927793942100948 | 0.3146293591732849 |
| 458.63 | 453.8324720669487 | 4.797527933051299 | 1.0837632617676214 |
| 449.93 | 453.30606496401464 | -3.376064964014631 | -0.7626542728669009 |
| 452.39 | 449.08008122232087 | 3.3099187776791155 | 0.7477118259115428 |
| 443.29 | 445.36190546480196 | -2.0719054648019437 | -0.4680441794071581 |
| 446.22 | 449.83432112958235 | -3.614321129582322 | -0.8164764251785102 |
| 439.0 | 444.60209183702824 | -5.60209183702824 | -1.2655145330562003 |
| 452.75 | 455.3330390953461 | -2.5830390953461233 | -0.5835094478470579 |
| 445.65 | 450.28095549994475 | -4.6309554999447755 | -1.046134490045332 |
| 445.27 | 449.3942290809264 | -4.124229080926398 | -0.9316648122523561 |
| 451.95 | 448.76188428428145 | 3.1881157157185385 | 0.7201964710108949 |
| 445.02 | 449.49526226001734 | -4.4752622600173595 | -1.0109633319210711 |
| 450.74 | 449.4914112072868 | 1.2485887927132353 | 0.2820566511504923 |
| 443.93 | 449.5171242611325 | -5.587124261132487 | -1.262133352352325 |
| 445.91 | 447.2640843894497 | -1.3540843894496675 | -0.30588814387272023 |
| 445.71 | 451.75573664689705 | -6.045736646897069 | -1.3657340529671387 |
| 452.7 | 449.47769197848703 | 3.2223080215129585 | 0.7279205250179235 |
| 449.38 | 448.9807967933042 | 0.3992032066958018 | 9.01801459906393e-2 |
| 445.09 | 452.83851864090616 | -7.748518640906184 | -1.7503931093934726 |
| 450.22 | 448.99070865761826 | 1.2292913423817708 | 0.27769735027577597 |
| 441.41 | 448.9314689751323 | -7.521468975132279 | -1.6991025093602443 |
| 462.19 | 458.19122302999057 | 3.9987769700094304 | 0.9033251358981357 |
| 446.17 | 449.6590005617065 | -3.4890005617064617 | -0.7881664644439343 |
| 444.19 | 446.545237358833 | -2.3552373588329942 | -0.532048954767078 |
| 453.15 | 456.3129527756159 | -3.1629527756159064 | -0.7145121539163726 |
| 451.49 | 449.9268730104538 | 1.5631269895462196 | 0.35311094138229787 |
| 453.38 | 448.0350183689646 | 5.3449816310354095 | 1.2074332463249917 |
| 452.1 | 453.06361431502756 | -0.9636143150275416 | -0.21768081556036123 |
| 446.33 | 449.51211271813366 | -3.1821127181336806 | -0.718840391727202 |
| 452.46 | 450.672947544889 | 1.7870524551109952 | 0.40369578348012486 |
| 444.87 | 447.9552056621943 | -3.085205662194312 | -0.6969490534174008 |
| 444.04 | 448.2666586698033 | -4.226658669803271 | -0.9548036927115915 |
| 449.12 | 448.66968221055265 | 0.4503177894473538 | 0.10172694836464449 |
| 443.15 | 445.7146703190735 | -2.564670319073514 | -0.5793599347716393 |
| 446.84 | 452.8021698612517 | -5.962169861251709 | -1.3468562864485707 |
| 443.93 | 446.7443660008083 | -2.8143660008083202 | -0.6357662778430159 |
| 455.18 | 448.49796091182566 | 6.682039088174349 | 1.5094750001492365 |
| 451.88 | 449.90863388073797 | 1.9713661192620293 | 0.44533230558821424 |
| 458.47 | 453.53620348431275 | 4.933796515687277 | 1.1145463829197875 |
| 449.26 | 451.60241101975805 | -2.3424110197580603 | -0.5291514802205262 |
| 444.16 | 448.28015134415546 | -4.120151344155431 | -0.9307436500694146 |
| 441.05 | 445.940139097113 | -4.890139097112979 | -1.1046841565784926 |
| 447.88 | 452.5181226448485 | -4.638122644848522 | -1.047753550621287 |
| 444.91 | 449.080854354919 | -4.1708543549189585 | -0.9421974781853397 |
| 439.01 | 443.764677908941 | -4.754677908941005 | -1.0740834261221295 |
| 453.17 | 449.2796285666682 | 3.890371433331836 | 0.8788362867110385 |
-- Now we can display the RMSE as a Histogram. Clearly this shows that the RMSE is centered around 0 with the vast majority of the error within 2 RMSEs.
SELECT Within_RSME from Power_Plant_RMSE_Evaluation
| Within_RSME |
|---|
| -0.6545286058914855 |
| -1.4178948518800556 |
| 0.17524239493619823 |
| 0.44163480044197995 |
| 0.26132139752130357 |
| 0.28066570579802425 |
| 1.3625651590092023 |
| 0.9078489886839033 |
| -0.3442308494884213 |
| -0.11630232674577932 |
| 1.7506729821805804 |
| -8.284953745386567e-2 |
| -1.692818871916148 |
| 2.608373132048146e-2 |
| 0.8783285919200484 |
| 1.8262340682999108 |
| 0.9515196517846441 |
| 0.9951418870719423 |
| -0.12833292050229034 |
| 1.8547757064958097 |
| -6.520499829010114e-2 |
| 1.7540228045303743 |
| 1.712912449331917 |
| -0.567063215206105 |
| 1.758470293691663 |
| 0.36292285373571487 |
| 1.3722994239368362 |
| -0.4518791902667791 |
| -0.711637096481805 |
| 0.7477551488923485 |
| -9.075206274161249e-2 |
| 0.33498359634397323 |
| -2.717087121436152 |
| 1.6347795140090344 |
| 0.5413939250993844 |
| 1.8721087453250556 |
| 1.2897763086344445 |
| -0.6298581141929721 |
| -0.3869554869711247 |
| 1.5590952476122018 |
| -5.164688385271067e-2 |
| 1.021860350507925 |
| 4.2073297187840204e-2 |
| 3.6816563372465104e-2 |
| 2.4273181887690556 |
| 0.2760063132279849 |
| -0.2711988922825122 |
| -0.49565448805243767 |
| 1.6046669568831393 |
| -0.5946690382578993 |
| 1.1099602752189677 |
| 0.42249584921569994 |
| 0.3776264153858503 |
| 1.1253464244922151 |
| 2.313691606156222 |
| 0.15702246269194528 |
| 1.1180526463224945 |
| 1.0326707046913566 |
| -0.5009264197587914 |
| 2.1055253303633577 |
| 0.883079580425271 |
| 0.8562239455498787 |
| 1.0581479782654901 |
| -0.5881175213603873 |
| -1.0469362575856187 |
| 0.9845051707078616 |
| -0.32033221960491765 |
| -6.11464435454739e-2 |
| 1.7966491781271794 |
| 1.449961394951398 |
| 0.1429791665816065 |
| 0.17796926331974147 |
| 0.590178010121793 |
| -0.4427473424095181 |
| -1.4586722394340677 |
| -0.14061563348410486 |
| -0.4778797517789611 |
| 0.16287170617772706 |
| 1.6205038004183012 |
| 0.6655668633335223 |
| 0.9552928474721311 |
| -0.9255614318409298 |
| -0.20841065925050425 |
| -0.5044470235035043 |
| -0.9064055675522111 |
| -0.5331117277702286 |
| 1.0604764411029242 |
| 0.6163555912803304 |
| 1.502475041125319 |
| 0.11025852973791857 |
| 1.027322682875218e-2 |
| 1.2068500286116204 |
| 0.7807519242835766 |
| 0.5378958494817917 |
| 1.4921557049542535 |
| 0.8423417649127792 |
| -0.8074228949228237 |
| -1.273287251952606 |
| -0.8366248339128552 |
| 0.5703862219885535 |
| 0.9915076732202934 |
| 1.392578067734949 |
| -1.1161712376708273 |
| -0.5177873225895746 |
| 1.2543530104022107 |
| 0.8332547944829863 |
| -0.15024202389261102 |
| -0.7461833621634392 |
| -2.6333071255094267 |
| 1.7090186657480726 |
| 1.17171695524459 |
| 0.16538389150158914 |
| -0.6043651708916123 |
| 1.5140936302699408 |
| -0.40248969630754267 |
| -0.5274788489010923 |
| 1.673819828943716 |
| 0.3998536629456538 |
| 1.5247431144210337 |
| 0.681669465709775 |
| -0.5067930534728822 |
| 1.3329317905059794 |
| 0.2815190232236669 |
| 0.9594271583094607 |
| 0.7195804749163808 |
| 1.563628994525865 |
| -0.5164095221527084 |
| 0.5408652916763936 |
| 1.2169907361146226 |
| 1.3691416206809235 |
| 0.9990582667496749 |
| 0.2866573009496584 |
| -0.1322508701375881 |
| -0.11350226422907182 |
| 1.5478953375265612 |
| 0.6261411303745675 |
| -0.3570008639436786 |
| 0.11097880108736301 |
| -1.997789295120947e-2 |
| 0.8015184563019332 |
| 1.0694995653480546 |
| -0.44658623651141477 |
| 1.5261372030315674 |
| 9.637974736911713e-2 |
| -0.8289854400119429 |
| 1.6226588690300885 |
| 1.2686705940133918 |
| -0.17369127241360752 |
| 1.465503060651426 |
| -0.8493901094223798 |
| -3.19863982083975e-3 |
| 0.775250166241454 |
| -1.476480677907779 |
| 1.6164395333609067 |
| -0.7292074491181413 |
| -2.0719934236347326 |
| 6.689462100697262e-2 |
| 0.41987991622217147 |
| 1.5698655082870474 |
| 0.9108140262190783 |
| 1.4283829990671366 |
| -0.7468033961054397 |
| 1.8565224554443056e-2 |
| -0.631158789082455 |
| -2.423658794064658e-2 |
| 0.1268789228414677 |
| 1.2467256943415417 |
| 0.9532625827884375 |
| 1.858427070746631 |
| 3.080438049074982 |
| -0.7935508099728905 |
| -0.33001241533337206 |
| 1.5352270267793358 |
| -0.8014312542128782 |
| 1.0796607821279804 |
| 0.8682110816429235 |
| 0.8502714264690703 |
| 0.271857163937664 |
| 1.4407225588170043 |
| -7.658599891256687e-2 |
| 0.4048751439362759 |
| -1.6508290646045036 |
| 0.9392995195813987 |
| 0.3512225476325008 |
| 1.3510289270963245e-2 |
| 1.214207255223955 |
| 1.357197397311141 |
| 0.7924624787403274 |
| 0.9747529486084029 |
| -0.2189330666129607 |
| 1.1957383378087658 |
| -0.33808517274788225 |
| 0.12841983400410018 |
| 2.6901995797195863e-2 |
| 0.6811317532938489 |
| -0.14977990817360218 |
| 0.3021002532054435 |
| 6.309664277182285e-2 |
| -1.4280877316330394 |
| -0.31036099175789383 |
| 0.23847593115995214 |
| -1.2544261801023373 |
| 1.487015909020143 |
| 1.4770822031817277e-2 |
| 1.5464079765310332 |
| -0.9661142653632034 |
| 0.12365480382094843 |
| 0.2213694799459989 |
| -0.371717945334288 |
| -7.16964482466269e-2 |
| -1.3370460279083884 |
| -0.4791979479015218 |
| 0.7258131397464832 |
| -0.9603785228015735 |
| 1.761740374853143 |
| 0.756705284425051 |
| -7.97943379405968e-2 |
| -0.12401745329336308 |
| 0.6066524918978817 |
| -1.827662052785967 |
| -1.591900543337891 |
| -2.2076662292962226 |
| -0.6113138148859982 |
| -0.6998705392764937 |
| -0.950596077665842 |
| 0.8530061355541199 |
| -0.3389719928622738 |
| 1.166670943264595 |
| -0.793497343451686 |
| -0.17702361157448407 |
| -1.2193083364383357 |
| -2.0572343953484604 |
| -0.3306196669058454 |
| 0.30773883474602903 |
| -0.23209777066088685 |
| 1.4407294606630792 |
| -0.25326355704561365 |
| -4.4359271514481574e-2 |
| 0.6878454023126037 |
| -1.1071350441930146 |
| 1.1936865467290878 |
| -0.3007124612846165 |
| 1.4096765924648265 |
| 0.6164656679054759 |
| 0.9857137966698644 |
| 1.0741485680349747 |
| -0.5956816180527323 |
| 0.42955444320288233 |
| 0.3977787059312943 |
| -0.21498609418317555 |
| 0.9605949185920998 |
| -0.9629884170069595 |
| 1.0232242351169787 |
| -0.5846231192914554 |
| 1.3665650089641064 |
| 1.746749569478921 |
| 0.20876984487810568 |
| 1.2521345827220822 |
| 0.5034033523179731 |
| 0.2428222722587704 |
| -0.8164837978146291 |
| 0.8816626861116392 |
| 0.3639355749527803 |
| 0.529553624374745 |
| -0.5182949859646964 |
| 1.1483434385624023 |
| 1.7878124840322416 |
| 1.1154300586630213 |
| 1.7220135063654567 |
| -0.5948541705999838 |
| 2.6840159315323495 |
| 1.4048914641301884 |
| 1.048062749037262 |
| 1.0587197080412232 |
| 2.305613395286325 |
| -0.36112584123348884 |
| -0.9292014386335458 |
| 1.3994718175237673 |
| -2.07160847091363 |
| 1.5499716375563712 |
| -0.49879093666907914 |
| -0.7387667159730148 |
| 1.215743751168344 |
| 0.41488138983573053 |
| 0.8109415219227073 |
| 0.17199705539157537 |
| 0.37238038749473606 |
| 1.0232860206712358 |
| 7.648152721354506e-2 |
| -1.462397837959548 |
| 1.1080799981170868 |
| -0.8241779060158768 |
| 0.6646125250731286 |
| -0.5357411851906123 |
| -1.1013185729957693 |
| 1.0228434545117104 |
| -2.2138675491121655 |
| 1.1090799040003696 |
| 0.528855769995572 |
| 0.5510063260220736 |
| -0.6178137048585499 |
| -1.5423062021582727 |
| 0.14223087889069697 |
| 0.6842724817647582 |
| 0.8947164246665408 |
| 0.2929208050392189 |
| 1.0509433168534397 |
| -0.7432089035850772 |
| -0.4404227630306511 |
| -1.749430050046347 |
| -1.8086930425174812 |
| -1.0601131663478296 |
| 1.1367001553490168 |
| 0.8092747458990647 |
| -0.6912565984961676 |
| 0.5876026195201212 |
| 0.588118260475777 |
| -1.5843005948416944 |
| 1.2258227543850637 |
| -2.221379961092406 |
| 0.634487138275479 |
| 0.5838507185000019 |
| -0.7046913242897344 |
| -0.37217013370970675 |
| -0.40920885267913865 |
| 0.6878875094589886 |
| -0.6603570138111878 |
| 0.47983904985387016 |
| 1.1108376145467862 |
| -1.2967007380916762 |
| 1.597843788860663 |
| -1.2003451799842708 |
| 0.7823286855941306 |
| 0.4040957861116898 |
| -0.8115843408453783 |
| -0.5164400209043697 |
| 0.6648789506575099 |
| 1.302692092451488 |
| 0.6081791894050589 |
| 0.9892527182470402 |
| 0.8010661900831437 |
| 1.3632196657801583 |
| -0.95536478346845 |
| 1.0773296627734226 |
| -1.2863290875171947 |
| -0.45717742235729764 |
| -1.3782059158917384 |
| 1.3292415395636934 |
| 0.898840426845182 |
| -0.49778501447760914 |
| -2.8013349681766146 |
| -0.4810779148314091 |
| -0.30893260677359485 |
| 1.4144446986124883 |
| 0.8455721466933415 |
| 1.149027173540852 |
| 1.398381410079887 |
| -2.07482799048434 |
| 0.802797467810633 |
| -1.5148720297345803 |
| 0.4457711691982717 |
| 0.8083230187436264 |
| -0.3652639670874125 |
| -1.301009165777192 |
| 0.12351025454739102 |
| 1.1417131567412993 |
| -0.36627110968185583 |
| 0.11986193019333387 |
| 0.9789636158092969 |
| 0.8287590631587972 |
| 0.12784192086705481 |
| -0.2933818764583589 |
| 0.37057045258220545 |
| 0.17867647626623853 |
| 1.4063653898508444 |
| 0.3724177883609634 |
| 0.47087976564737744 |
| -0.29605954815653446 |
| 0.3100202222752913 |
| -0.7571192782768728 |
| -0.159362104405368 |
| 0.4948614777371899 |
| -0.914693478650593 |
| 0.1552739582781331 |
| -0.47397010386004174 |
| 0.9853374925715822 |
| -0.27611557009437565 |
| -0.11876915655659566 |
| -1.9139681704240736 |
| 4.953016947212214e-3 |
| 1.1193515979151634 |
| 0.10914706208866293 |
| 0.2953624624059635 |
| -0.6203480029931175 |
| 1.447373814412471 |
| -1.6476543672481883 |
| -0.688655110626592 |
| 1.119882332695939 |
| -0.489107443803926 |
| 0.36504587152451 |
| -0.6898805978802584 |
| -1.0090851773780105 |
| 1.3164462111456963 |
| -0.9719462634817788 |
| -1.195980679378918 |
| -0.2324693067978492 |
| 1.5483799402320506 |
| -0.7562589912210242 |
| 9.006543612814395e-2 |
| 0.6307060325245487 |
| -0.2658735764273784 |
| 1.3036368729747179 |
| 1.3861689239960837 |
| -0.10256413058630598 |
| 1.7873934636501403 |
| 0.39996285217557137 |
| -0.962793977380421 |
| 1.2120179370324367 |
| -0.14370787512306984 |
| 0.1873907498457296 |
| 8.883711718846676e-2 |
| 1.1389205017346977 |
| -1.5633041348778628e-2 |
| 1.5435878241412961 |
| 0.9211075789572168 |
| 0.17182562184847788 |
| -1.0405053204973118 |
| 0.6134953430761453 |
| 0.924316094817409 |
| 0.893158168449016 |
| 0.7054993963391207 |
| 1.1169128153424053 |
| 1.2716543656809969 |
| 0.2909263677211387 |
| 0.3156983081862408 |
| -0.3181585485837661 |
| -1.79557827837392 |
| -1.9821852874504897 |
| -0.18315266781990724 |
| 0.29419068616581573 |
| 1.321272422372547 |
| -0.5300977560633852 |
| 0.7301877034905364 |
| 0.35702870934871034 |
| -0.12785771898138407 |
| -1.4568475152442806 |
| 1.1799216369387262 |
| -2.6289350896996853 |
| 1.36938729411264 |
| 0.8658222795557158 |
| -1.961929096387823 |
| -1.7869367782036754 |
| -2.3414119790965118 |
| -0.7070000881461707 |
| 1.094870026989326 |
| 1.1674921223009784 |
| 0.4309575170361471 |
| -0.5608691138957983 |
| 0.9204597474090102 |
| 0.45642908946816274 |
| 1.3262603156304236 |
| 0.6694839282628849 |
| -1.4191468918247268 |
| -2.0503769708593955 |
| -1.559496185458982 |
| -0.8294949891974716 |
| -0.7511122251047269 |
| 0.9255039510005931 |
| -0.7348543381165886 |
| 0.14006818598115695 |
| -8.906989903365076e-2 |
| 0.5960428781510217 |
| -1.02587326449146 |
| -0.19021607612190924 |
| 0.4462389970391413 |
| -0.4412430070274576 |
| -0.5519946201974366 |
| -0.5978564569550795 |
| 1.3285941918109334 |
| 0.9398258266993826 |
| 0.18054354406665346 |
| 0.24526741726554308 |
| 0.6419203730291116 |
| -0.9563898027913122 |
| -0.4574416919740238 |
| -0.49100134055869604 |
| 0.8350348745649541 |
| 1.1230180632103244 |
| -0.2831261948065207 |
| -0.34737057487344897 |
| -4.4197222396898606e-2 |
| 0.4029451403943494 |
| -0.8387790498308709 |
| -0.76724104350462 |
| -1.405536067872571 |
| -0.5049830367049549 |
| -0.4450784100952837 |
| -1.0964525416975441 |
| -0.24679765115367583 |
| 0.6373400044210754 |
| 0.47325677731185334 |
| -2.895796152379444 |
| 1.0608917942097844 |
| -0.5538446783382351 |
| -1.0739516871848376 |
| 0.6590103675152038 |
| 0.19886190778235394 |
| -0.7684000719143101 |
| -0.9540651350015262 |
| 0.8326866968950778 |
| 1.049265553847453 |
| 1.1564460495859417 |
| -0.9105747398006214 |
| -1.298779737770393 |
| 0.7486049675853867 |
| 1.0125992544722646 |
| -0.552486882193573 |
| 4.083638455563342e-2 |
| -7.43032114617912e-2 |
| 0.38180618893011364 |
| -2.5821601917725587 |
| -3.9183934552884936e-2 |
| -0.7161579504665817 |
| -1.1179393167374423 |
| 0.24473358069515785 |
| 0.37566200116631454 |
| -0.8469696321106422 |
| 0.9536067209045733 |
| 1.040389080973342e-3 |
| -1.9463087774264596 |
| -0.5212577234014426 |
| 2.6198974509955283e-2 |
| -0.22312751932133126 |
| -0.3749580186284632 |
| 0.8158971199557807 |
| 0.5526973095848743 |
| -1.0007418364636667 |
| -0.7893531119518733 |
| 0.948603975708334 |
| -0.5535013570539268 |
| 0.615437313273332 |
| 0.390353931433719 |
| 2.1370626071593804e-2 |
| -4.58603577983067e-2 |
| -1.8585598276086635 |
| -1.7168393323476852 |
| -1.1398906775971094 |
| -0.4823380615634837 |
| 0.96139463327236 |
| -0.28409897841626214 |
| -1.1427184843899973 |
| 0.47191339234564744 |
| -0.7999147952790192 |
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We can see this definitively if we count the number of predictions within + or - 1.0 and + or - 2.0 and display this as a pie chart:
SELECT case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1 when Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end RSME_Multiple, COUNT(*) count from Power_Plant_RMSE_Evaluation
group by case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1 when Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end
| RSME_Multiple | count |
|---|---|
| 1.0 | 1267.0 |
| 3.0 | 77.0 |
| 2.0 | 512.0 |
So we have about 70% of our training data within 1 RMSE and about 97% (70% + 27%) within 2 RMSE. So the model is pretty decent. Let's see if we can tune the model to improve it further.
NOTE: these numbers will vary across runs due to the seed in random sampling of training and test set, number of iterations, and other stopping rules in optimization, for example.
Step 7: Tuning and Evaluation
Now that we have a model with all of the data let's try to make a better model by tuning over several parameters.
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._
First let's use a cross validator to split the data into training and validation subsets. See http://spark.apache.org/docs/latest/ml-tuning.html.
//Let's set up our evaluator class to judge the model based on the best root mean squared error
val regEval = new RegressionEvaluator()
regEval.setLabelCol("PE")
.setPredictionCol("Predicted_PE")
.setMetricName("rmse")
regEval: org.apache.spark.ml.evaluation.RegressionEvaluator = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false
res101: regEval.type = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false
We now treat the lrPipeline as an Estimator, wrapping it in a CrossValidator instance.
This will allow us to jointly choose parameters for all Pipeline stages.
A CrossValidator requires an Estimator, an Evaluator (which we set next).
//Let's create our crossvalidator with 3 fold cross validation
val crossval = new CrossValidator()
crossval.setEstimator(lrPipeline)
crossval.setNumFolds(3)
crossval.setEvaluator(regEval)
crossval: org.apache.spark.ml.tuning.CrossValidator = cv_bc136566b6a6
res102: crossval.type = cv_bc136566b6a6
A CrossValidator also requires a set of EstimatorParamMaps which we set next.
For this we need a regularization parameter (more generally a hyper-parameter that is model-specific).
Now, let's tune over our regularization parameter from 0.01 to 0.10.
val regParam = ((1 to 10) toArray).map(x => (x /100.0))
warning: there was one feature warning; for details, enable `:setting -feature' or `:replay -feature'
regParam: Array[Double] = Array(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1)
Check out the scala docs for syntactic details on org.apache.spark.ml.tuning.ParamGridBuilder.
val paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam, regParam)
.build()
crossval.setEstimatorParamMaps(paramGrid)
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
linReg_3b8dffe4015f-regParam: 0.01
}, {
linReg_3b8dffe4015f-regParam: 0.02
}, {
linReg_3b8dffe4015f-regParam: 0.03
}, {
linReg_3b8dffe4015f-regParam: 0.04
}, {
linReg_3b8dffe4015f-regParam: 0.05
}, {
linReg_3b8dffe4015f-regParam: 0.06
}, {
linReg_3b8dffe4015f-regParam: 0.07
}, {
linReg_3b8dffe4015f-regParam: 0.08
}, {
linReg_3b8dffe4015f-regParam: 0.09
}, {
linReg_3b8dffe4015f-regParam: 0.1
})
res103: crossval.type = cv_bc136566b6a6
//Now let's create our model
val cvModel = crossval.fit(trainingSet)
cvModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_07136143d91d, numFolds=3
In addition to CrossValidator Spark also offers TrainValidationSplit for hyper-parameter tuning. TrainValidationSplit only evaluates each combination of parameters once as opposed to k times in case of CrossValidator. It is therefore less expensive, but will not produce as reliable results when the training dataset is not sufficiently large.
Now that we have tuned let's see what we got for tuning parameters and what our RMSE was versus our intial model
val predictionsAndLabels = cvModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@671c9416
rmse: Double = 4.4286032895695895
explainedVariance: Double = 271.5750243564875
r2: Double = 0.9313732865178349
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 4.4286032895695895
Explained Variance: 271.5750243564875
R2: 0.9313732865178349
Let us explore other models to see if we can predict the power output better
There are several families of models in Spark's scalable machine learning library:
So our initial untuned and tuned linear regression models are statistically identical.
Given that the only linearly correlated variable is Temperature, it makes sense try another machine learning method such a Decision Tree to handle non-linear data and see if we can improve our model
A Decision Tree creates a model based on splitting variables using a tree structure. We will first start with a single decision tree model.
Reference Decision Trees: https://en.wikipedia.org/wiki/Decisiontreelearning
//Let's build a decision tree pipeline
import org.apache.spark.ml.regression.DecisionTreeRegressor
// we are using a Decision Tree Regressor as opposed to a classifier we used for the hand-written digit classification problem
val dt = new DecisionTreeRegressor()
dt.setLabelCol("PE")
dt.setPredictionCol("Predicted_PE")
dt.setFeaturesCol("features")
dt.setMaxBins(100)
val dtPipeline = new Pipeline()
dtPipeline.setStages(Array(vectorizer, dt))
import org.apache.spark.ml.regression.DecisionTreeRegressor
dt: org.apache.spark.ml.regression.DecisionTreeRegressor = dtr_92691f930484
dtPipeline: org.apache.spark.ml.Pipeline = pipeline_ddfeb1cfff04
res106: dtPipeline.type = pipeline_ddfeb1cfff04
//Let's just resuse our CrossValidator
crossval.setEstimator(dtPipeline)
res107: crossval.type = cv_bc136566b6a6
val paramGrid = new ParamGridBuilder()
.addGrid(dt.maxDepth, Array(2, 3))
.build()
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
dtr_92691f930484-maxDepth: 2
}, {
dtr_92691f930484-maxDepth: 3
})
crossval.setEstimatorParamMaps(paramGrid)
res108: crossval.type = cv_bc136566b6a6
val dtModel = crossval.fit(trainingSet) // fit decitionTree with cv
dtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_ddfeb1cfff04, numFolds=3
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel].toDebugString
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
res109: String =
"DecisionTreeRegressionModel: uid=dtr_92691f930484, depth=3, numNodes=15, numFeatures=4
If (feature 0 <= 18.595)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 8.575)
Predict: 483.994943457189
Else (feature 0 > 8.575)
Predict: 476.04374262101527
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 15.475000000000001)
Predict: 467.5564649956784
Else (feature 0 > 15.475000000000001)
Predict: 459.5010376134889
Else (feature 0 > 18.595)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 22.055)
Predict: 452.01076923076914
Else (feature 0 > 22.055)
Predict: 443.46937926330156
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: 440.73731707317074
Else (feature 0 > 25.325)
Predict: 433.86131215469624
"
The line above will pull the Decision Tree model from the Pipeline and display it as an if-then-else string.
Next let's visualize it as a decision tree for regression.
display(dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel])
| treeNode |
|---|
| {"index":7,"featureType":"continuous","prediction":null,"threshold":18.595,"categories":null,"feature":0,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":11.885000000000002,"categories":null,"feature":0,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":8.575,"categories":null,"feature":0,"overflow":false} |
| {"index":0,"featureType":null,"prediction":483.994943457189,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":2,"featureType":null,"prediction":476.04374262101527,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":15.475000000000001,"categories":null,"feature":0,"overflow":false} |
| {"index":4,"featureType":null,"prediction":467.5564649956784,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":459.5010376134889,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":66.21000000000001,"categories":null,"feature":1,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":22.055,"categories":null,"feature":0,"overflow":false} |
| {"index":8,"featureType":null,"prediction":452.01076923076914,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":10,"featureType":null,"prediction":443.46937926330156,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":13,"featureType":"continuous","prediction":null,"threshold":25.325,"categories":null,"feature":0,"overflow":false} |
| {"index":12,"featureType":null,"prediction":440.73731707317074,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":14,"featureType":null,"prediction":433.86131215469624,"threshold":null,"categories":null,"feature":null,"overflow":false} |
Now let's see how our DecisionTree model compares to our LinearRegression model
val predictionsAndLabels = dtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 5.111944542729953
Explained Variance: 261.4355220034205
R2: 0.908560906504635
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@684c4c50
rmse: Double = 5.111944542729953
explainedVariance: Double = 261.4355220034205
r2: Double = 0.908560906504635
So our DecisionTree was slightly worse than our LinearRegression model (LR: 4.6 vs DT: 5.2). Maybe we can try an Ensemble method such as Gradient-Boosted Decision Trees to see if we can strengthen our model by using an ensemble of weaker trees with weighting to reduce the error in our model.
*Note since this is a complex model, the cell below can take about *16 minutes* or so to run on a small cluster with a couple nodes with about 6GB RAM, go out and grab a coffee and come back :-).*
This GBTRegressor code will be way faster on a larger cluster of course.
A visual explanation of gradient boosted trees:
Let's see what a boosting algorithm, a type of ensemble method, is all about in more detail.
import org.apache.spark.ml.regression.GBTRegressor
val gbt = new GBTRegressor()
gbt.setLabelCol("PE")
gbt.setPredictionCol("Predicted_PE")
gbt.setFeaturesCol("features")
gbt.setSeed(100088121L)
gbt.setMaxBins(100)
gbt.setMaxIter(120)
val gbtPipeline = new Pipeline()
gbtPipeline.setStages(Array(vectorizer, gbt))
//Let's just resuse our CrossValidator
crossval.setEstimator(gbtPipeline)
val paramGrid = new ParamGridBuilder()
.addGrid(gbt.maxDepth, Array(2, 3))
.build()
crossval.setEstimatorParamMaps(paramGrid)
//gbt.explainParams
val gbtModel = crossval.fit(trainingSet)
import org.apache.spark.ml.regression.GBTRegressor
gbt: org.apache.spark.ml.regression.GBTRegressor = gbtr_0a275b363831
gbtPipeline: org.apache.spark.ml.Pipeline = pipeline_8b2722444cff
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
gbtr_0a275b363831-maxDepth: 2
}, {
gbtr_0a275b363831-maxDepth: 3
})
gbtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
import org.apache.spark.ml.regression.GBTRegressionModel
val predictionsAndLabels = gbtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 3.7034735091301307
Explained Variance: 272.208177448154
R2: 0.9520069744321176
import org.apache.spark.ml.regression.GBTRegressionModel
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@79d7946a
rmse: Double = 3.7034735091301307
explainedVariance: Double = 272.208177448154
r2: Double = 0.9520069744321176
We can use the toDebugString method to dump out what our trees and weighting look like:
gbtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[GBTRegressionModel].toDebugString
res116: String =
"GBTRegressionModel: uid=gbtr_0a275b363831, numTrees=120, numFeatures=4
Tree 0 (weight 1.0):
If (feature 0 <= 18.595)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 8.575)
Predict: 483.994943457189
Else (feature 0 > 8.575)
Predict: 476.04374262101527
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 15.475000000000001)
Predict: 467.5564649956784
Else (feature 0 > 15.475000000000001)
Predict: 459.5010376134889
Else (feature 0 > 18.595)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 22.055)
Predict: 452.01076923076914
Else (feature 0 > 22.055)
Predict: 443.46937926330156
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: 440.73731707317074
Else (feature 0 > 25.325)
Predict: 433.86131215469624
Tree 1 (weight 0.1):
If (feature 3 <= 82.55000000000001)
If (feature 0 <= 26.505000000000003)
If (feature 3 <= 64.11500000000001)
Predict: 6.095232469256877
Else (feature 3 > 64.11500000000001)
Predict: 1.4337156041793564
Else (feature 0 > 26.505000000000003)
If (feature 1 <= 65.55000000000001)
Predict: -7.6527692217902885
Else (feature 1 > 65.55000000000001)
Predict: -0.5844786594493007
Else (feature 3 > 82.55000000000001)
If (feature 1 <= 45.754999999999995)
If (feature 3 <= 93.075)
Predict: 0.6108402960070604
Else (feature 3 > 93.075)
Predict: -3.913147108789527
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 18.595)
Predict: -9.443118256805649
Else (feature 0 > 18.595)
Predict: -3.9650066253122347
Tree 2 (weight 0.1):
If (feature 1 <= 45.754999999999995)
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 14.285)
Predict: 1.1090321642566017
Else (feature 0 > 14.285)
Predict: -3.898383406004269
Else (feature 0 > 15.475000000000001)
If (feature 0 <= 18.595)
Predict: 4.874047316205584
Else (feature 0 > 18.595)
Predict: 10.653531335032094
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 18.595)
If (feature 1 <= 58.19)
Predict: -5.0005796708960615
Else (feature 1 > 58.19)
Predict: -13.749053154552547
Else (feature 0 > 18.595)
If (feature 2 <= 1009.295)
Predict: -3.1410617295379555
Else (feature 2 > 1009.295)
Predict: 0.8001319329379373
Tree 3 (weight 0.1):
If (feature 3 <= 85.52000000000001)
If (feature 1 <= 55.825)
If (feature 0 <= 26.505000000000003)
Predict: 2.695149105254423
Else (feature 0 > 26.505000000000003)
Predict: -6.7564705067621675
Else (feature 1 > 55.825)
If (feature 1 <= 66.21000000000001)
Predict: -2.282355645191304
Else (feature 1 > 66.21000000000001)
Predict: 0.5508297910243797
Else (feature 3 > 85.52000000000001)
If (feature 1 <= 40.629999999999995)
If (feature 2 <= 1022.8399999999999)
Predict: 2.236331037122985
Else (feature 2 > 1022.8399999999999)
Predict: -7.220151471077029
Else (feature 1 > 40.629999999999995)
If (feature 3 <= 89.595)
Predict: -2.175085332960937
Else (feature 3 > 89.595)
Predict: -4.54715329410012
Tree 4 (weight 0.1):
If (feature 2 <= 1010.335)
If (feature 1 <= 43.005)
If (feature 0 <= 15.475000000000001)
Predict: -0.004818717215773318
Else (feature 0 > 15.475000000000001)
Predict: 5.355639866401019
Else (feature 1 > 43.005)
If (feature 1 <= 65.15)
Predict: -5.132340755716604
Else (feature 1 > 65.15)
Predict: -0.7228608840382099
Else (feature 2 > 1010.335)
If (feature 3 <= 74.525)
If (feature 0 <= 29.455)
Predict: 3.0313483965828176
Else (feature 0 > 29.455)
Predict: -2.560863167947768
Else (feature 3 > 74.525)
If (feature 1 <= 40.629999999999995)
Predict: 2.228142113797423
Else (feature 1 > 40.629999999999995)
Predict: -1.6052323952407273
Tree 5 (weight 0.1):
If (feature 3 <= 81.045)
If (feature 0 <= 27.585)
If (feature 3 <= 46.92)
Predict: 8.005444802948366
Else (feature 3 > 46.92)
Predict: 1.301219291279602
Else (feature 0 > 27.585)
If (feature 1 <= 65.55000000000001)
Predict: -6.568175152421089
Else (feature 1 > 65.55000000000001)
Predict: -0.8331220915230161
Else (feature 3 > 81.045)
If (feature 1 <= 41.519999999999996)
If (feature 2 <= 1019.835)
Predict: 1.7054425589091675
Else (feature 2 > 1019.835)
Predict: -2.6266728146418195
Else (feature 1 > 41.519999999999996)
If (feature 1 <= 41.805)
Predict: -11.130585327952137
Else (feature 1 > 41.805)
Predict: -2.111742422947989
Tree 6 (weight 0.1):
If (feature 2 <= 1009.625)
If (feature 1 <= 43.005)
If (feature 0 <= 15.475000000000001)
Predict: -0.4497329015715072
Else (feature 0 > 15.475000000000001)
Predict: 3.661440765591046
Else (feature 1 > 43.005)
If (feature 1 <= 66.85499999999999)
Predict: -3.861085082339984
Else (feature 1 > 66.85499999999999)
Predict: -0.7492674320362704
Else (feature 2 > 1009.625)
If (feature 3 <= 69.32499999999999)
If (feature 0 <= 29.455)
Predict: 2.621042446270476
Else (feature 0 > 29.455)
Predict: -1.4554021183385886
Else (feature 3 > 69.32499999999999)
If (feature 1 <= 58.19)
Predict: 0.42605173456002876
Else (feature 1 > 58.19)
Predict: -1.9554878629891888
Tree 7 (weight 0.1):
If (feature 3 <= 86.625)
If (feature 1 <= 52.795)
If (feature 0 <= 18.595)
Predict: 0.4988124937300424
Else (feature 0 > 18.595)
Predict: 4.447321094438702
Else (feature 1 > 52.795)
If (feature 1 <= 66.21000000000001)
Predict: -1.721618872277871
Else (feature 1 > 66.21000000000001)
Predict: 0.6365209437219329
Else (feature 3 > 86.625)
If (feature 0 <= 6.895)
If (feature 3 <= 87.32499999999999)
Predict: -10.224737687790185
Else (feature 3 > 87.32499999999999)
Predict: 3.712660431036073
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
Predict: -7.105091794629204
Else (feature 0 > 8.575)
Predict: -1.5060749360589718
Tree 8 (weight 0.1):
If (feature 2 <= 1008.745)
If (feature 1 <= 41.665)
If (feature 0 <= 15.475000000000001)
Predict: 0.11492519898093705
Else (feature 0 > 15.475000000000001)
Predict: 3.532699993937119
Else (feature 1 > 41.665)
If (feature 1 <= 66.85499999999999)
Predict: -3.366654434213031
Else (feature 1 > 66.85499999999999)
Predict: -0.9165259817521934
Else (feature 2 > 1008.745)
If (feature 3 <= 82.955)
If (feature 1 <= 51.905)
Predict: 1.841638760642105
Else (feature 1 > 51.905)
Predict: 0.33265178659776373
Else (feature 3 > 82.955)
If (feature 3 <= 95.68)
Predict: -0.4850135884105616
Else (feature 3 > 95.68)
Predict: -3.812639024859598
Tree 9 (weight 0.1):
If (feature 0 <= 29.455)
If (feature 3 <= 61.89)
If (feature 1 <= 71.18)
Predict: 1.9301726185107941
Else (feature 1 > 71.18)
Predict: 8.938327477606107
Else (feature 3 > 61.89)
If (feature 0 <= 5.8149999999999995)
Predict: 4.428862111265314
Else (feature 0 > 5.8149999999999995)
Predict: -0.3308347482908286
Else (feature 0 > 29.455)
If (feature 1 <= 68.28999999999999)
If (feature 2 <= 1009.875)
Predict: -8.941194411728706
Else (feature 2 > 1009.875)
Predict: -1.8834474815204216
Else (feature 1 > 68.28999999999999)
If (feature 2 <= 1014.405)
Predict: -0.5760094442636617
Else (feature 2 > 1014.405)
Predict: -5.50575933697771
Tree 10 (weight 0.1):
If (feature 2 <= 1005.345)
If (feature 0 <= 27.355)
If (feature 3 <= 99.3)
Predict: -0.5544332197918127
Else (feature 3 > 99.3)
Predict: -6.049841121821514
Else (feature 0 > 27.355)
If (feature 1 <= 70.815)
Predict: -8.03479585317622
Else (feature 1 > 70.815)
Predict: 0.0619269945107018
Else (feature 2 > 1005.345)
If (feature 3 <= 72.41499999999999)
If (feature 0 <= 24.755000000000003)
Predict: 2.020104454165069
Else (feature 0 > 24.755000000000003)
Predict: -0.22964512858748945
Else (feature 3 > 72.41499999999999)
If (feature 1 <= 40.7)
Predict: 1.2987210163143512
Else (feature 1 > 40.7)
Predict: -0.8347660227231797
Tree 11 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 1 <= 42.3)
If (feature 1 <= 39.765)
Predict: 4.261094449524424
Else (feature 1 > 39.765)
Predict: 9.140536590115639
Else (feature 1 > 42.3)
If (feature 2 <= 1007.835)
Predict: 0.9746298984436711
Else (feature 2 > 1007.835)
Predict: -6.317973546286112
Else (feature 0 > 5.0600000000000005)
If (feature 3 <= 95.68)
If (feature 2 <= 1009.295)
Predict: -0.8626626541525325
Else (feature 2 > 1009.295)
Predict: 0.3906915507169364
Else (feature 3 > 95.68)
If (feature 0 <= 11.885000000000002)
Predict: -6.002679638413293
Else (feature 0 > 11.885000000000002)
Predict: -0.7509189525586508
Tree 12 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 51.905)
If (feature 0 <= 13.695)
Predict: 0.5958576966099496
Else (feature 0 > 13.695)
Predict: -1.2694566117501789
Else (feature 1 > 51.905)
If (feature 1 <= 58.19)
Predict: -4.248373100253223
Else (feature 1 > 58.19)
Predict: -9.144729000689791
Else (feature 0 > 18.595)
If (feature 1 <= 47.64)
If (feature 2 <= 1012.675)
Predict: 0.8560563666774771
Else (feature 2 > 1012.675)
Predict: 10.801233083620462
Else (feature 1 > 47.64)
If (feature 0 <= 20.015)
Predict: 3.1978561830060213
Else (feature 0 > 20.015)
Predict: -0.16716555702980626
Tree 13 (weight 0.1):
If (feature 0 <= 28.655)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 58.19)
Predict: 0.34765099851241454
Else (feature 1 > 58.19)
Predict: -1.8189329025195076
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 22.525)
Predict: 6.490484815153365
Else (feature 0 > 22.525)
Predict: 0.7933381751314565
Else (feature 0 > 28.655)
If (feature 1 <= 68.28999999999999)
If (feature 2 <= 1005.665)
Predict: -8.922459399148678
Else (feature 2 > 1005.665)
Predict: -2.3989441108132668
Else (feature 1 > 68.28999999999999)
If (feature 2 <= 1018.215)
Predict: -0.3023338438804105
Else (feature 2 > 1018.215)
Predict: 14.559949112833806
Tree 14 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
If (feature 0 <= 11.885000000000002)
Predict: -0.8897676968273979
Else (feature 0 > 11.885000000000002)
Predict: 3.5874467728768487
Else (feature 0 > 13.695)
If (feature 1 <= 46.345)
Predict: -3.2690852418064273
Else (feature 1 > 46.345)
Predict: -8.844433418899179
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 43.175)
If (feature 1 <= 41.805)
Predict: 3.298855216614116
Else (feature 1 > 41.805)
Predict: 9.150659154207167
Else (feature 1 > 43.175)
If (feature 2 <= 1012.495)
Predict: -0.7109832273625656
Else (feature 2 > 1012.495)
Predict: 1.1631179843236674
Tree 15 (weight 0.1):
If (feature 3 <= 90.055)
If (feature 0 <= 30.41)
If (feature 1 <= 66.21000000000001)
Predict: -0.01732155577258642
Else (feature 1 > 66.21000000000001)
Predict: 1.3432966941310502
Else (feature 0 > 30.41)
If (feature 1 <= 69.465)
Predict: -3.6657811330207344
Else (feature 1 > 69.465)
Predict: -0.38803561342243853
Else (feature 3 > 90.055)
If (feature 2 <= 1015.725)
If (feature 1 <= 47.64)
Predict: 1.5063080039677825
Else (feature 1 > 47.64)
Predict: -1.860635777865782
Else (feature 2 > 1015.725)
If (feature 1 <= 40.7)
Predict: 0.21921885078759318
Else (feature 1 > 40.7)
Predict: -4.793242614118129
Tree 16 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -1.8477082180199003
Else (feature 1 > 39.620000000000005)
Predict: 16.387935166882745
Else (feature 3 > 67.42500000000001)
If (feature 0 <= 5.0600000000000005)
Predict: 4.152127693563297
Else (feature 0 > 5.0600000000000005)
Predict: 0.7556214745509566
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 2 <= 1022.8399999999999)
Predict: -2.9436476590152036
Else (feature 2 > 1022.8399999999999)
Predict: -8.032234733606465
Else (feature 0 > 8.575)
If (feature 0 <= 9.325)
Predict: 5.082738415532321
Else (feature 0 > 9.325)
Predict: -0.032287806549855816
Tree 17 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 0 <= 28.655)
If (feature 0 <= 11.885000000000002)
Predict: -4.455566502931916
Else (feature 0 > 11.885000000000002)
Predict: 1.9027943448616742
Else (feature 0 > 28.655)
If (feature 1 <= 66.21000000000001)
Predict: -3.1810487244675034
Else (feature 1 > 66.21000000000001)
Predict: 0.11525363261816625
Else (feature 3 > 61.89)
If (feature 1 <= 43.175)
If (feature 0 <= 18.595)
Predict: 0.22262511130066664
Else (feature 0 > 18.595)
Predict: 10.13639446335034
Else (feature 1 > 43.175)
If (feature 0 <= 22.055)
Predict: -1.4775024201129299
Else (feature 0 > 22.055)
Predict: 0.20591189539330298
Tree 18 (weight 0.1):
If (feature 2 <= 1005.345)
If (feature 1 <= 70.815)
If (feature 0 <= 23.564999999999998)
Predict: -0.178777362918765
Else (feature 0 > 23.564999999999998)
Predict: -4.52113806132068
Else (feature 1 > 70.815)
If (feature 3 <= 60.025000000000006)
Predict: 4.247605743793766
Else (feature 3 > 60.025000000000006)
Predict: -0.26865379510738685
Else (feature 2 > 1005.345)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 55.825)
Predict: 0.3773109266288433
Else (feature 1 > 55.825)
Predict: -1.3659380946007862
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 21.335)
Predict: 8.448170645066464
Else (feature 0 > 21.335)
Predict: 0.5048679905700459
Tree 19 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 51.905)
If (feature 0 <= 17.655)
Predict: -0.003054757854134698
Else (feature 0 > 17.655)
Predict: -2.9290357116654935
Else (feature 1 > 51.905)
If (feature 1 <= 66.525)
Predict: -4.061825439604536
Else (feature 1 > 66.525)
Predict: -11.67844591879286
Else (feature 0 > 18.595)
If (feature 0 <= 23.945)
If (feature 3 <= 74.525)
Predict: 3.807909998319029
Else (feature 3 > 74.525)
Predict: 0.008459259251505081
Else (feature 0 > 23.945)
If (feature 1 <= 74.915)
Predict: -0.6429811796826012
Else (feature 1 > 74.915)
Predict: 2.099670946504916
Tree 20 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 14.285)
If (feature 0 <= 11.885000000000002)
Predict: -0.7234702732306101
Else (feature 0 > 11.885000000000002)
Predict: 1.589299673192479
Else (feature 0 > 14.285)
If (feature 2 <= 1016.495)
Predict: -2.4381805412578545
Else (feature 2 > 1016.495)
Predict: -6.544687605774689
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 43.005)
If (feature 2 <= 1008.405)
Predict: 0.8119248626873221
Else (feature 2 > 1008.405)
Predict: 5.506769369239865
Else (feature 1 > 43.005)
If (feature 2 <= 1012.935)
Predict: -0.5028175384892806
Else (feature 2 > 1012.935)
Predict: 1.0290531238165197
Tree 21 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.9752219334588972
Else (feature 1 > 39.620000000000005)
Predict: 13.369575512848845
Else (feature 3 > 67.42500000000001)
If (feature 1 <= 42.705)
Predict: 1.850492181024754
Else (feature 1 > 42.705)
Predict: -2.033710059657357
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 2 <= 1022.8399999999999)
Predict: -2.256120115332435
Else (feature 2 > 1022.8399999999999)
Predict: -6.380948299395909
Else (feature 0 > 8.575)
If (feature 0 <= 9.594999999999999)
Predict: 3.4448565562285904
Else (feature 0 > 9.594999999999999)
Predict: -0.05858832726578875
Tree 22 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 21.095)
If (feature 1 <= 66.21000000000001)
Predict: -0.17987193554240402
Else (feature 1 > 66.21000000000001)
Predict: 6.044335675519052
Else (feature 0 > 21.095)
If (feature 1 <= 66.21000000000001)
Predict: -5.809637597188877
Else (feature 1 > 66.21000000000001)
Predict: 2.7761443044302214
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
If (feature 1 <= 64.74000000000001)
Predict: 5.951769778696803
Else (feature 1 > 64.74000000000001)
Predict: -0.30197045172071896
Else (feature 0 > 23.564999999999998)
If (feature 1 <= 44.519999999999996)
Predict: -7.283292958317056
Else (feature 1 > 44.519999999999996)
Predict: -0.19387573131258415
Tree 23 (weight 0.1):
If (feature 3 <= 53.025000000000006)
If (feature 0 <= 24.945)
If (feature 0 <= 18.29)
Predict: 1.0381040338364682
Else (feature 0 > 18.29)
Predict: 5.164469183375362
Else (feature 0 > 24.945)
If (feature 1 <= 66.21000000000001)
Predict: -1.0707595335847206
Else (feature 1 > 66.21000000000001)
Predict: 0.8108674466901084
Else (feature 3 > 53.025000000000006)
If (feature 1 <= 73.725)
If (feature 1 <= 71.505)
Predict: -0.0525992725981239
Else (feature 1 > 71.505)
Predict: -2.807221561152817
Else (feature 1 > 73.725)
If (feature 2 <= 1017.295)
Predict: 1.037788102485777
Else (feature 2 > 1017.295)
Predict: 7.287181806639116
Tree 24 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 70.815)
If (feature 1 <= 59.144999999999996)
Predict: -0.3743851611431973
Else (feature 1 > 59.144999999999996)
Predict: -4.648812647772385
Else (feature 1 > 70.815)
If (feature 0 <= 27.884999999999998)
Predict: 2.4545579195950995
Else (feature 0 > 27.884999999999998)
Predict: -0.32777974793969294
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
Predict: 0.045218490740101897
Else (feature 0 > 20.795)
Predict: -3.3620385127109333
Else (feature 0 > 22.055)
If (feature 0 <= 22.955)
Predict: 4.485324626081587
Else (feature 0 > 22.955)
Predict: 0.13166549369330485
Tree 25 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 58.19)
If (feature 0 <= 17.655)
Predict: -0.023636417468335308
Else (feature 0 > 17.655)
Predict: -2.607492744918568
Else (feature 1 > 58.19)
If (feature 1 <= 66.525)
Predict: -4.8994505786627665
Else (feature 1 > 66.525)
Predict: -10.07085237281708
Else (feature 0 > 18.595)
If (feature 0 <= 19.725)
If (feature 1 <= 66.21000000000001)
Predict: 2.2716539497960406
Else (feature 1 > 66.21000000000001)
Predict: 13.879740983230867
Else (feature 0 > 19.725)
If (feature 1 <= 43.005)
Predict: 5.616411926544602
Else (feature 1 > 43.005)
Predict: -0.00532316539213451
Tree 26 (weight 0.1):
If (feature 3 <= 93.075)
If (feature 1 <= 55.825)
If (feature 0 <= 22.055)
Predict: 0.15441544139943786
Else (feature 0 > 22.055)
Predict: 2.7626508552722306
Else (feature 1 > 55.825)
If (feature 1 <= 66.21000000000001)
Predict: -1.2060290652635515
Else (feature 1 > 66.21000000000001)
Predict: 0.4827535356971057
Else (feature 3 > 93.075)
If (feature 2 <= 1015.725)
If (feature 0 <= 7.34)
Predict: 4.241421869391143
Else (feature 0 > 7.34)
Predict: -0.704916847045625
Else (feature 2 > 1015.725)
If (feature 0 <= 11.885000000000002)
Predict: -5.4904249010577795
Else (feature 0 > 11.885000000000002)
Predict: 0.0976872424106726
Tree 27 (weight 0.1):
If (feature 2 <= 1008.745)
If (feature 1 <= 41.665)
If (feature 1 <= 39.765)
Predict: -1.0511879426704696
Else (feature 1 > 39.765)
Predict: 1.894883627758776
Else (feature 1 > 41.665)
If (feature 1 <= 41.805)
Predict: -13.576686832482961
Else (feature 1 > 41.805)
Predict: -0.7168167899342832
Else (feature 2 > 1008.745)
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
Predict: 0.303140697564446
Else (feature 0 > 13.695)
Predict: -2.8034862119109945
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 55.825)
Predict: 2.0736158373993576
Else (feature 1 > 55.825)
Predict: -0.03336709818500243
Tree 28 (weight 0.1):
If (feature 2 <= 1001.785)
If (feature 3 <= 69.86500000000001)
If (feature 1 <= 69.465)
Predict: -7.299995136853619
Else (feature 1 > 69.465)
Predict: 0.23757420536270835
Else (feature 3 > 69.86500000000001)
If (feature 0 <= 7.34)
Predict: 3.4313360513286284
Else (feature 0 > 7.34)
Predict: -1.276734468456009
Else (feature 2 > 1001.785)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 11.335)
Predict: 0.04499264982858887
Else (feature 0 > 11.335)
Predict: -6.156735713959919
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 12.715)
Predict: 4.4864210547378915
Else (feature 0 > 12.715)
Predict: 0.030721690290287165
Tree 29 (weight 0.1):
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 26.505000000000003)
If (feature 0 <= 22.055)
Predict: -0.3694472776628706
Else (feature 0 > 22.055)
Predict: 1.5208925945902059
Else (feature 0 > 26.505000000000003)
If (feature 1 <= 43.510000000000005)
Predict: -17.99975152056241
Else (feature 1 > 43.510000000000005)
Predict: -2.4033598663496183
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 22.055)
If (feature 0 <= 18.595)
Predict: -7.600012529438
Else (feature 0 > 18.595)
Predict: 6.469471961408998
Else (feature 0 > 22.055)
If (feature 0 <= 25.325)
Predict: -2.6540186758683166
Else (feature 0 > 25.325)
Predict: 0.9869775581610103
Tree 30 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 1 <= 42.3)
If (feature 3 <= 95.68)
Predict: 2.6307256050737506
Else (feature 3 > 95.68)
Predict: 7.168878406232186
Else (feature 1 > 42.3)
If (feature 2 <= 1007.835)
Predict: -0.1884983669158752
Else (feature 2 > 1007.835)
Predict: -5.920088632100639
Else (feature 0 > 5.0600000000000005)
If (feature 1 <= 37.815)
If (feature 0 <= 11.885000000000002)
Predict: -3.8096010917307517
Else (feature 0 > 11.885000000000002)
Predict: 3.5943708074284917
Else (feature 1 > 37.815)
If (feature 1 <= 41.519999999999996)
Predict: 0.6752927073561888
Else (feature 1 > 41.519999999999996)
Predict: -0.14342050966250913
Tree 31 (weight 0.1):
If (feature 3 <= 99.3)
If (feature 0 <= 31.155)
If (feature 3 <= 46.92)
Predict: 2.011051499336945
Else (feature 3 > 46.92)
Predict: 0.012791339921714258
Else (feature 0 > 31.155)
If (feature 2 <= 1014.405)
Predict: -0.916397796921109
Else (feature 2 > 1014.405)
Predict: -6.832504867736499
Else (feature 3 > 99.3)
If (feature 1 <= 39.765)
If (feature 1 <= 38.41)
Predict: -5.505405887296888
Else (feature 1 > 38.41)
Predict: -16.748187635942333
Else (feature 1 > 39.765)
If (feature 0 <= 16.04)
Predict: 0.7708563952983728
Else (feature 0 > 16.04)
Predict: -3.909609799859729
Tree 32 (weight 0.1):
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 64.74000000000001)
If (feature 2 <= 1011.515)
Predict: -1.0002623040885374
Else (feature 2 > 1011.515)
Predict: 0.3591184679910596
Else (feature 1 > 64.74000000000001)
If (feature 2 <= 1006.775)
Predict: -13.788703659238209
Else (feature 2 > 1006.775)
Predict: -2.4620285364725656
Else (feature 1 > 66.21000000000001)
If (feature 1 <= 69.09)
If (feature 0 <= 22.525)
Predict: 6.088217885246919
Else (feature 0 > 22.525)
Predict: 1.0364537580784474
Else (feature 1 > 69.09)
If (feature 0 <= 25.325)
Predict: -2.7946704533493856
Else (feature 0 > 25.325)
Predict: 0.6607665941219878
Tree 33 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 0 <= 29.455)
If (feature 1 <= 66.21000000000001)
Predict: -0.08158010506593574
Else (feature 1 > 66.21000000000001)
Predict: 0.8815313937000332
Else (feature 0 > 29.455)
If (feature 1 <= 44.519999999999996)
Predict: -12.581939252812768
Else (feature 1 > 44.519999999999996)
Predict: -0.77008278158028
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 0.6766771966451415
Else (feature 1 > 39.45)
Predict: -8.445008186341592
Else (feature 0 > 8.575)
If (feature 0 <= 8.96)
Predict: 3.777292703648982
Else (feature 0 > 8.96)
Predict: -2.398045803854934
Tree 34 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.6804590872791323
Else (feature 1 > 39.620000000000005)
Predict: 10.518093452986212
Else (feature 3 > 67.42500000000001)
If (feature 1 <= 42.705)
Predict: 1.3098801739379287
Else (feature 1 > 42.705)
Predict: -1.7450883352732074
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 0 <= 7.765)
Predict: -0.7816329288490581
Else (feature 0 > 7.765)
Predict: -3.7319393815256547
Else (feature 0 > 8.575)
If (feature 0 <= 9.965)
Predict: 2.5600814387681337
Else (feature 0 > 9.965)
Predict: -0.06944896856946733
Tree 35 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 1 <= 39.34)
If (feature 0 <= 16.695)
Predict: -4.078381955056753
Else (feature 0 > 16.695)
Predict: -16.616821684050763
Else (feature 1 > 39.34)
If (feature 1 <= 40.82)
Predict: 4.4394084324272045
Else (feature 1 > 40.82)
Predict: 0.4663514281701948
Else (feature 3 > 61.89)
If (feature 1 <= 58.19)
If (feature 0 <= 22.055)
Predict: -0.027831559557693095
Else (feature 0 > 22.055)
Predict: 2.492136574233702
Else (feature 1 > 58.19)
If (feature 0 <= 18.595)
Predict: -4.183073089901298
Else (feature 0 > 18.595)
Predict: -0.40866909936948326
Tree 36 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 38.754999999999995)
If (feature 3 <= 77.195)
Predict: 9.579430817769946
Else (feature 3 > 77.195)
Predict: 2.6305173165509195
Else (feature 1 > 38.754999999999995)
If (feature 1 <= 70.065)
Predict: -1.801035005150415
Else (feature 1 > 70.065)
Predict: 0.4054557218074593
Else (feature 2 > 1004.505)
If (feature 2 <= 1017.475)
If (feature 1 <= 43.175)
Predict: 1.09341093192285
Else (feature 1 > 43.175)
Predict: -0.03229585395374685
Else (feature 2 > 1017.475)
If (feature 2 <= 1019.365)
Predict: -1.7719467091167656
Else (feature 2 > 1019.365)
Predict: 0.2537098831339789
Tree 37 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
If (feature 0 <= 11.885000000000002)
Predict: -0.5104239863875834
Else (feature 0 > 11.885000000000002)
Predict: 2.1202163252308432
Else (feature 0 > 13.695)
If (feature 3 <= 78.38499999999999)
Predict: -3.379040627299855
Else (feature 3 > 78.38499999999999)
Predict: -0.9911440360990582
Else (feature 0 > 15.475000000000001)
If (feature 0 <= 16.395)
If (feature 3 <= 67.42500000000001)
Predict: 0.4887402058080506
Else (feature 3 > 67.42500000000001)
Predict: 3.884289185845989
Else (feature 0 > 16.395)
If (feature 2 <= 1020.565)
Predict: -0.02862374251288089
Else (feature 2 > 1020.565)
Predict: 3.15734851002617
Tree 38 (weight 0.1):
If (feature 3 <= 43.385)
If (feature 0 <= 24.945)
If (feature 2 <= 1014.405)
Predict: 1.603183026814036
Else (feature 2 > 1014.405)
Predict: 7.05673098720603
Else (feature 0 > 24.945)
If (feature 0 <= 26.295)
Predict: -5.886157652325024
Else (feature 0 > 26.295)
Predict: 0.7387886738447553
Else (feature 3 > 43.385)
If (feature 1 <= 73.725)
If (feature 1 <= 71.505)
Predict: 0.008754170391068655
Else (feature 1 > 71.505)
Predict: -2.0178119354056765
Else (feature 1 > 73.725)
If (feature 1 <= 77.42)
Predict: 1.8396223170900134
Else (feature 1 > 77.42)
Predict: -1.5747478023010713
Tree 39 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
If (feature 0 <= 18.595)
Predict: -0.25201916420658693
Else (feature 0 > 18.595)
Predict: 1.5718496558845116
Else (feature 0 > 20.795)
If (feature 1 <= 66.21000000000001)
Predict: -3.7201562729508804
Else (feature 1 > 66.21000000000001)
Predict: 2.087916157719636
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
If (feature 2 <= 1012.015)
Predict: 0.7371264627237751
Else (feature 2 > 1012.015)
Predict: 4.6381746481309065
Else (feature 0 > 23.564999999999998)
If (feature 1 <= 44.519999999999996)
Predict: -5.772062593218147
Else (feature 1 > 44.519999999999996)
Predict: -0.12977023255758624
Tree 40 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 3 <= 95.68)
If (feature 2 <= 1011.105)
Predict: -3.365769010003795
Else (feature 2 > 1011.105)
Predict: 2.955050770650336
Else (feature 3 > 95.68)
If (feature 2 <= 1008.205)
Predict: 2.728200788704399
Else (feature 2 > 1008.205)
Predict: 6.919382935391042
Else (feature 0 > 5.0600000000000005)
If (feature 3 <= 96.61)
If (feature 1 <= 37.815)
Predict: -1.3949762323292278
Else (feature 1 > 37.815)
Predict: 0.06411431759031856
Else (feature 3 > 96.61)
If (feature 0 <= 11.605)
Predict: -3.491961189446582
Else (feature 0 > 11.605)
Predict: -0.16406940197018208
Tree 41 (weight 0.1):
If (feature 0 <= 30.41)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 58.85)
Predict: 0.15540001609212736
Else (feature 1 > 58.85)
Predict: -1.1352706510871309
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: -0.6390388689881783
Else (feature 0 > 25.325)
Predict: 1.3909471658872326
Else (feature 0 > 30.41)
If (feature 2 <= 1014.405)
If (feature 1 <= 56.894999999999996)
Predict: -9.841212730443857
Else (feature 1 > 56.894999999999996)
Predict: -0.5054201979116707
Else (feature 2 > 1014.405)
If (feature 1 <= 74.915)
Predict: -5.898616989217175
Else (feature 1 > 74.915)
Predict: 1.042314191558674
Tree 42 (weight 0.1):
If (feature 2 <= 1009.625)
If (feature 1 <= 43.005)
If (feature 1 <= 42.025000000000006)
Predict: -0.05077288158998095
Else (feature 1 > 42.025000000000006)
Predict: 3.619244461816007
Else (feature 1 > 43.005)
If (feature 0 <= 18.595)
Predict: -3.5057172058309307
Else (feature 0 > 18.595)
Predict: -0.32537979322253646
Else (feature 2 > 1009.625)
If (feature 2 <= 1017.475)
If (feature 1 <= 55.825)
Predict: 0.8076710222538108
Else (feature 1 > 55.825)
Predict: -0.01784249647067053
Else (feature 2 > 1017.475)
If (feature 2 <= 1019.365)
Predict: -1.4312272708033218
Else (feature 2 > 1019.365)
Predict: 0.20844195286029432
Tree 43 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 0 <= 17.655)
If (feature 0 <= 15.475000000000001)
Predict: -0.2913115404147692
Else (feature 0 > 15.475000000000001)
Predict: 1.1145997107947745
Else (feature 0 > 17.655)
If (feature 1 <= 37.815)
Predict: 14.538914074443028
Else (feature 1 > 37.815)
Predict: -3.0407663665320683
Else (feature 0 > 18.595)
If (feature 2 <= 1020.325)
If (feature 1 <= 43.175)
Predict: 3.881117973012625
Else (feature 1 > 43.175)
Predict: 0.04538590552170286
Else (feature 2 > 1020.325)
If (feature 1 <= 64.74000000000001)
Predict: 4.662857871344832
Else (feature 1 > 64.74000000000001)
Predict: -43.5190245896606
Tree 44 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.7153417257252007
Else (feature 1 > 39.620000000000005)
Predict: 8.417772324738223
Else (feature 3 > 67.42500000000001)
If (feature 2 <= 1010.985)
Predict: 2.5111312207852263
Else (feature 2 > 1010.985)
Predict: 0.29950092637128345
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 1 <= 40.06)
Predict: -3.30672576579827
Else (feature 1 > 40.06)
Predict: -0.45097750406116727
Else (feature 0 > 8.575)
If (feature 0 <= 9.325)
Predict: 3.226884779601992
Else (feature 0 > 9.325)
Predict: -0.041218673012550146
Tree 45 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 0 <= 10.395)
If (feature 3 <= 92.22999999999999)
Predict: 1.0355113706204075
Else (feature 3 > 92.22999999999999)
Predict: -1.4603765116034264
Else (feature 0 > 10.395)
If (feature 0 <= 11.885000000000002)
Predict: -2.630098257038007
Else (feature 0 > 11.885000000000002)
Predict: 0.09582680902226459
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 1.107359449029309
Else (feature 1 > 39.45)
Predict: -6.096185488604492
Else (feature 0 > 8.575)
If (feature 3 <= 70.57499999999999)
Predict: -3.516006231223605
Else (feature 3 > 70.57499999999999)
Predict: -0.26144006873351155
Tree 46 (weight 0.1):
If (feature 0 <= 27.884999999999998)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 25.145)
Predict: 0.06412914679730906
Else (feature 0 > 25.145)
Predict: -1.554041618425865
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: -0.4748636523867588
Else (feature 0 > 25.325)
Predict: 2.4595627925276826
Else (feature 0 > 27.884999999999998)
If (feature 3 <= 61.89)
If (feature 1 <= 71.895)
Predict: -0.4429995884239883
Else (feature 1 > 71.895)
Predict: 1.4321627506429122
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
Predict: -0.2617373838209719
Else (feature 1 > 71.505)
Predict: -2.637373068367584
Tree 47 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 1 <= 39.34)
If (feature 0 <= 16.695)
Predict: -2.855821258258964
Else (feature 0 > 16.695)
Predict: -13.80709248590955
Else (feature 1 > 39.34)
If (feature 1 <= 74.915)
Predict: 0.35443616318343385
Else (feature 1 > 74.915)
Predict: 2.9400682899293233
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
If (feature 1 <= 66.85499999999999)
Predict: -0.142107366817925
Else (feature 1 > 66.85499999999999)
Predict: 1.0352328327059535
Else (feature 1 > 71.505)
If (feature 1 <= 73.00999999999999)
Predict: -2.947898208728739
Else (feature 1 > 73.00999999999999)
Predict: -0.0830706492691125
Tree 48 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 38.754999999999995)
If (feature 3 <= 86.625)
Predict: 4.921331281961159
Else (feature 3 > 86.625)
Predict: -5.961237317667383
Else (feature 1 > 38.754999999999995)
If (feature 1 <= 39.765)
Predict: -4.436072018762161
Else (feature 1 > 39.765)
Predict: -0.7283906418193187
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
Predict: 0.044899959208437666
Else (feature 0 > 20.795)
Predict: -2.2295677836603995
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
Predict: 2.3559950404053414
Else (feature 0 > 23.564999999999998)
Predict: -0.06950420285239005
Tree 49 (weight 0.1):
If (feature 1 <= 41.435)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 11.335)
Predict: -0.02697483446966382
Else (feature 0 > 11.335)
Predict: -3.7031660865730367
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 13.265)
Predict: 4.209724879257512
Else (feature 0 > 13.265)
Predict: 0.41602586770224464
Else (feature 1 > 41.435)
If (feature 1 <= 41.805)
If (feature 3 <= 89.595)
Predict: -1.6099299736449546
Else (feature 3 > 89.595)
Predict: -9.419799062932288
Else (feature 1 > 41.805)
If (feature 1 <= 43.175)
Predict: 1.5951066240527068
Else (feature 1 > 43.175)
Predict: -0.11481901338423915
Tree 50 (weight 0.1):
If (feature 1 <= 38.41)
If (feature 0 <= 11.885000000000002)
If (feature 2 <= 1016.665)
Predict: -5.344526613859499
Else (feature 2 > 1016.665)
Predict: 0.058134490545794976
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 16.985)
Predict: 1.537246393097642
Else (feature 0 > 16.985)
Predict: 9.920778277565365
Else (feature 1 > 38.41)
If (feature 0 <= 13.485)
If (feature 1 <= 51.045)
Predict: 0.6675749827847329
Else (feature 1 > 51.045)
Predict: -5.737026170963195
Else (feature 0 > 13.485)
If (feature 0 <= 15.475000000000001)
Predict: -1.7210713764803844
Else (feature 0 > 15.475000000000001)
Predict: 0.09027853735147576
Tree 51 (weight 0.1):
If (feature 0 <= 31.155)
If (feature 3 <= 48.230000000000004)
If (feature 2 <= 1020.325)
Predict: 0.9307527734280435
Else (feature 2 > 1020.325)
Predict: 9.070547343579028
Else (feature 3 > 48.230000000000004)
If (feature 1 <= 73.725)
Predict: -0.06359121142988887
Else (feature 1 > 73.725)
Predict: 1.049949579330099
Else (feature 0 > 31.155)
If (feature 1 <= 63.08)
If (feature 1 <= 44.519999999999996)
Predict: -6.355864033256125
Else (feature 1 > 44.519999999999996)
Predict: 13.6760008529786
Else (feature 1 > 63.08)
If (feature 1 <= 68.28999999999999)
Predict: -3.953494984806905
Else (feature 1 > 68.28999999999999)
Predict: -0.6145841300086397
Tree 52 (weight 0.1):
If (feature 2 <= 1012.495)
If (feature 1 <= 41.435)
If (feature 1 <= 39.975)
Predict: -0.4047468839922722
Else (feature 1 > 39.975)
Predict: 2.509624688414308
Else (feature 1 > 41.435)
If (feature 1 <= 41.805)
Predict: -6.811044477667833
Else (feature 1 > 41.805)
Predict: -0.26889174065489546
Else (feature 2 > 1012.495)
If (feature 3 <= 92.22999999999999)
If (feature 1 <= 58.19)
Predict: 0.7048711166950787
Else (feature 1 > 58.19)
Predict: -0.5475390646726656
Else (feature 3 > 92.22999999999999)
If (feature 1 <= 41.805)
Predict: -3.7013459723577355
Else (feature 1 > 41.805)
Predict: 0.7237930378019226
Tree 53 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 0 <= 17.335)
If (feature 3 <= 74.08500000000001)
Predict: -0.8631764946312587
Else (feature 3 > 74.08500000000001)
Predict: 0.2803856631344212
Else (feature 0 > 17.335)
If (feature 1 <= 42.705)
Predict: 0.9335711174385192
Else (feature 1 > 42.705)
Predict: -2.950020164379197
Else (feature 0 > 18.595)
If (feature 2 <= 1013.395)
If (feature 1 <= 66.85499999999999)
Predict: -0.7231135633124072
Else (feature 1 > 66.85499999999999)
Predict: 0.41724670068145925
Else (feature 2 > 1013.395)
If (feature 1 <= 58.19)
Predict: 4.568024116358373
Else (feature 1 > 58.19)
Predict: -0.36270787813043714
Tree 54 (weight 0.1):
If (feature 2 <= 1001.785)
If (feature 3 <= 63.575)
If (feature 3 <= 60.025000000000006)
Predict: -1.7427137986580719
Else (feature 3 > 60.025000000000006)
Predict: -7.776342039375448
Else (feature 3 > 63.575)
If (feature 0 <= 27.119999999999997)
Predict: 0.026174663094801796
Else (feature 0 > 27.119999999999997)
Predict: -2.343138620856655
Else (feature 2 > 1001.785)
If (feature 0 <= 22.055)
If (feature 1 <= 66.21000000000001)
Predict: -0.27236570115397085
Else (feature 1 > 66.21000000000001)
Predict: 3.164590339421908
Else (feature 0 > 22.055)
If (feature 0 <= 22.725)
Predict: 2.9597120242025485
Else (feature 0 > 22.725)
Predict: 0.054881549113388446
Tree 55 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 1 <= 45.754999999999995)
If (feature 0 <= 26.715)
Predict: 0.2599176823406179
Else (feature 0 > 26.715)
Predict: -5.549910372715466
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 15.475000000000001)
Predict: -2.867925531108855
Else (feature 0 > 15.475000000000001)
Predict: -0.0236838100703647
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 0.7777761733424086
Else (feature 1 > 39.45)
Predict: -5.202001886405932
Else (feature 0 > 8.575)
If (feature 3 <= 68.945)
Predict: -3.641368616696809
Else (feature 3 > 68.945)
Predict: -0.6008141057791702
Tree 56 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 0 <= 18.29)
If (feature 2 <= 1019.025)
Predict: -2.1632736300070996
Else (feature 2 > 1019.025)
Predict: 1.2430029950309498
Else (feature 0 > 18.29)
If (feature 1 <= 44.91)
Predict: 3.2134324628571003
Else (feature 1 > 44.91)
Predict: 0.2988556025240279
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
If (feature 1 <= 66.85499999999999)
Predict: -0.09866504525517336
Else (feature 1 > 66.85499999999999)
Predict: 0.7271499788275563
Else (feature 1 > 71.505)
If (feature 1 <= 73.00999999999999)
Predict: -2.395971400707892
Else (feature 1 > 73.00999999999999)
Predict: -0.16608323426526406
Tree 57 (weight 0.1):
If (feature 2 <= 1017.645)
If (feature 2 <= 1014.615)
If (feature 1 <= 43.175)
Predict: 1.0389282601473917
Else (feature 1 > 43.175)
Predict: -0.3646675630154698
Else (feature 2 > 1014.615)
If (feature 1 <= 43.510000000000005)
Predict: -0.6889797697082773
Else (feature 1 > 43.510000000000005)
Predict: 1.5908362409321974
Else (feature 2 > 1017.645)
If (feature 2 <= 1019.025)
If (feature 1 <= 71.18)
Predict: -1.6569133708803008
Else (feature 1 > 71.18)
Predict: 4.314967971455512
Else (feature 2 > 1019.025)
If (feature 3 <= 89.595)
Predict: 0.43765066032139976
Else (feature 3 > 89.595)
Predict: -1.7344432288008194
Tree 58 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 70.815)
If (feature 1 <= 59.834999999999994)
Predict: -0.170418541124326
Else (feature 1 > 59.834999999999994)
Predict: -2.895038840312831
Else (feature 1 > 70.815)
If (feature 2 <= 1000.505)
Predict: -1.3832637115340176
Else (feature 2 > 1000.505)
Predict: 1.2249185299155396
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 1 <= 66.21000000000001)
Predict: -0.2418541439735617
Else (feature 1 > 66.21000000000001)
Predict: 3.0110894160107886
Else (feature 0 > 22.055)
If (feature 0 <= 23.945)
Predict: 1.4999960684883906
Else (feature 0 > 23.945)
Predict: -0.042407486119460915
Tree 59 (weight 0.1):
If (feature 1 <= 77.42)
If (feature 1 <= 74.915)
If (feature 1 <= 71.505)
Predict: 0.03983464447255206
Else (feature 1 > 71.505)
Predict: -0.7683021525819648
Else (feature 1 > 74.915)
If (feature 3 <= 72.82)
Predict: 2.774179202497669
Else (feature 3 > 72.82)
Predict: -0.32979787820368633
Else (feature 1 > 77.42)
If (feature 0 <= 21.595)
If (feature 0 <= 21.335)
Predict: -13.565760771414489
Else (feature 0 > 21.335)
Predict: -13.954507897669714
Else (feature 0 > 21.595)
If (feature 0 <= 22.955)
Predict: 10.853700477067264
Else (feature 0 > 22.955)
Predict: -1.4643467280880287
Tree 60 (weight 0.1):
If (feature 0 <= 10.395)
If (feature 1 <= 40.629999999999995)
If (feature 3 <= 75.545)
Predict: -2.3127871072788375
Else (feature 3 > 75.545)
Predict: 0.3992391857664209
Else (feature 1 > 40.629999999999995)
If (feature 3 <= 89.595)
Predict: 2.9152362525803523
Else (feature 3 > 89.595)
Predict: -1.4086879927580456
Else (feature 0 > 10.395)
If (feature 0 <= 11.885000000000002)
If (feature 2 <= 1025.2150000000001)
Predict: -2.2352340341897547
Else (feature 2 > 1025.2150000000001)
Predict: 5.952010328542228
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 12.415)
Predict: 3.4957190546300447
Else (feature 0 > 12.415)
Predict: -0.03992973893781255
Tree 61 (weight 0.1):
If (feature 1 <= 40.82)
If (feature 1 <= 40.224999999999994)
If (feature 3 <= 75.545)
Predict: -1.2944988036912455
Else (feature 3 > 75.545)
Predict: 0.44866615475817667
Else (feature 1 > 40.224999999999994)
If (feature 2 <= 1025.2150000000001)
Predict: 0.7826360308981203
Else (feature 2 > 1025.2150000000001)
Predict: 9.875672812487991
Else (feature 1 > 40.82)
If (feature 2 <= 1025.2150000000001)
If (feature 0 <= 10.945)
Predict: 1.1628421208118285
Else (feature 0 > 10.945)
Predict: -0.12551627485602937
Else (feature 2 > 1025.2150000000001)
If (feature 1 <= 41.15)
Predict: -7.782369290305258
Else (feature 1 > 41.15)
Predict: -1.200273276366743
Tree 62 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 21.335)
If (feature 1 <= 66.21000000000001)
Predict: -0.09621281022933233
Else (feature 1 > 66.21000000000001)
Predict: 2.680549669942832
Else (feature 0 > 21.335)
If (feature 1 <= 63.864999999999995)
Predict: -3.207766197974041
Else (feature 1 > 63.864999999999995)
Predict: -0.14162445042909583
Else (feature 0 > 22.055)
If (feature 0 <= 22.725)
If (feature 2 <= 1011.515)
Predict: 0.5064350504779975
Else (feature 2 > 1011.515)
Predict: 3.659612527058891
Else (feature 0 > 22.725)
If (feature 3 <= 75.545)
Predict: 0.2595183211083464
Else (feature 3 > 75.545)
Predict: -0.6564588168227348
Tree 63 (weight 0.1):
If (feature 2 <= 1017.645)
If (feature 1 <= 44.67)
If (feature 1 <= 44.13)
Predict: 0.3869693354075677
Else (feature 1 > 44.13)
Predict: 4.723132901950317
Else (f...
Conclusion
Wow! So our best model is in fact our Gradient Boosted Decision tree model which uses an ensemble of 120 Trees with a depth of 3 to construct a better model than the single decision tree.
Step 8: Deployment will be done later
Now that we have a predictive model it is time to deploy the model into an operational environment.
In our example, let's say we have a series of sensors attached to the power plant and a monitoring station.
The monitoring station will need close to real-time information about how much power that their station will generate so they can relay that to the utility.
For this we need to create a Spark Streaming utility that we can use for this purpose. For this you need to be introduced to basic concepts in Spark Streaming first. See http://spark.apache.org/docs/latest/streaming-programming-guide.html if you can't wait!
After deployment you will be able to use the best predictions from gradient boosed regression trees to feed a real-time dashboard or feed the utility with information on how much power the peaker plant will deliver give current conditions.
Persisting Statistical Machine Learning Models
See https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html
Let's save our best model so we can load it without having to rerun the validation and training again.
gbtModel
res117: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
gbtModel.bestModel.asInstanceOf[PipelineModel]
.write.overwrite().save("dbfs:///databricks/driver/MyTrainedBestPipelineModel")
When it is time to deploy the trained model to serve predicitons, we can simply reload this model and proceed as shown later.
This is one of the Deploymnet Patterns for Serving a Model's Prediction.
Datasource References:
- Pinar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615, Web Link
- Heysem Kaya, Pinar Tüfekci , Sadik Fikret Gürgen: Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine, Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE 2012, pp. 13-18 (Mar. 2012, Dubai) Web Link
Activity Recognition from Accelerometer
- ETL Using SparkSQL Windows
- Prediction using Random Forest
This work is a simpler databricksification of Amira Lakhal's more complex framework for activity recognition:
See Section below on 0. Download and Load Data first.
- Once data is loaded come back here (if you have not downloaded and loaded data into the distributed file system under your hood).
val data = sc.textFile("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv") // assumes data is loaded
data: org.apache.spark.rdd.RDD[String] = dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv MapPartitionsRDD[1] at textFile at command-561044440962565:1
data.take(5).foreach(println)
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
val dataDF = sqlContext.read
.format("com.databricks.spark.csv") // use spark.csv package
.option("header", "true") // Use first line of all files as header
.option("inferSchema", "true") // Automatically infer data types
.option("delimiter", ",") // Specify the delimiter as ','
.load("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
dataDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
val dataDFnew = spark.read.format("csv")
.option("inferSchema", "true")
.option("header", "true")
.option("sep", ",")
.load("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
dataDFnew: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
dataDFnew.printSchema()
root
|-- user_id: string (nullable = true)
|-- activity: string (nullable = true)
|-- timeStampAsLong: long (nullable = true)
|-- x: double (nullable = true)
|-- y: double (nullable = true)
|-- z: double (nullable = true)
display(dataDF) // zp.show(dataDF)
| user_id | activity | timeStampAsLong | x | y | z |
|---|---|---|---|---|---|
| user_001 | Jumping | 1.446047227606e12 | 4.33079 | -12.72175 | -3.18118 |
| user_001 | Jumping | 1.446047227671e12 | 0.575403 | -0.727487 | 2.95007 |
| user_001 | Jumping | 1.446047227735e12 | -1.60885 | 3.52607 | -0.1922 |
| user_001 | Jumping | 1.446047227799e12 | 0.690364 | -3.7722e-2 | 1.72382 |
| user_001 | Jumping | 1.446047227865e12 | 3.44943 | -1.68549 | 2.29862 |
| user_001 | Jumping | 1.44604722793e12 | 1.87829 | -1.91542 | 0.880768 |
| user_001 | Jumping | 1.446047227995e12 | 1.57173 | -5.86241 | -3.75599 |
| user_001 | Jumping | 1.446047228059e12 | 3.41111 | -17.93331 | 0.535886 |
| user_001 | Jumping | 1.446047228123e12 | 3.18118 | -19.58108 | 5.74745 |
| user_001 | Jumping | 1.446047228189e12 | 7.85626 | -19.2362 | 0.804128 |
| user_001 | Jumping | 1.446047228253e12 | 1.26517 | -8.85139 | 2.18366 |
| user_001 | Jumping | 1.446047228318e12 | 7.7239e-2 | 1.15021 | 1.53221 |
| user_001 | Jumping | 1.446047228383e12 | 0.230521 | 2.0699 | -1.41845 |
| user_001 | Jumping | 1.446047228447e12 | 0.652044 | -0.497565 | 1.76214 |
| user_001 | Jumping | 1.446047228512e12 | 1.53341 | -0.305964 | 1.41725 |
| user_001 | Jumping | 1.446047228578e12 | -1.07237 | -1.95374 | 0.191003 |
| user_001 | Jumping | 1.446047228642e12 | 2.75966 | -13.75639 | 0.191003 |
| user_001 | Jumping | 1.446047228707e12 | 7.43474 | -19.58108 | 2.95007 |
| user_001 | Jumping | 1.446047228771e12 | 5.63368 | -19.58108 | 5.59417 |
| user_001 | Jumping | 1.446047228836e12 | 5.02056 | -11.72542 | -1.38013 |
| user_001 | Jumping | 1.4460472289e12 | -2.10702 | 0.575403 | 1.07237 |
| user_001 | Jumping | 1.446047228966e12 | -1.30229 | 2.2615 | -1.26517 |
| user_001 | Jumping | 1.44604722903e12 | 1.68669 | -0.957409 | 1.57053 |
| user_001 | Jumping | 1.446047229095e12 | 2.60638 | -0.229323 | 2.14534 |
| user_001 | Jumping | 1.446047229159e12 | 1.30349 | -0.152682 | 0.497565 |
| user_001 | Jumping | 1.446047229224e12 | 1.64837 | -8.12331 | 1.60885 |
| user_001 | Jumping | 1.44604722929e12 | 0.15388 | -18.46979 | -1.03525 |
| user_001 | Jumping | 1.446047229354e12 | 4.98224 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047229419e12 | 5.17384 | -19.2362 | 0.612526 |
| user_001 | Jumping | 1.446047229483e12 | 2.03158 | -9.11964 | 1.34061 |
| user_001 | Jumping | 1.446047229549e12 | -1.95374 | -1.03405 | 0.574206 |
| user_001 | Jumping | 1.446047229612e12 | 0.1922 | 1.30349 | -1.03525 |
| user_001 | Jumping | 1.446047229678e12 | 1.64837 | -0.727487 | -0.307161 |
| user_001 | Jumping | 1.446047229743e12 | 2.56806 | -1.03405 | 0.267643 |
| user_001 | Jumping | 1.446047229806e12 | 1.41845 | -0.305964 | 0.727487 |
| user_001 | Jumping | 1.446047229872e12 | 1.03525 | -6.82042 | 0.957409 |
| user_001 | Jumping | 1.446047229936e12 | 3.94759 | -19.31284 | -1.22685 |
| user_001 | Jumping | 1.446047230001e12 | 6.13185 | -19.58108 | 2.72014 |
| user_001 | Jumping | 1.446047230066e12 | 7.89458 | -16.9753 | 0.229323 |
| user_001 | Jumping | 1.446047230131e12 | 2.6447 | -3.75479 | 0.114362 |
| user_001 | Jumping | 1.446047230195e12 | -2.41358 | 1.91661 | -5.99e-4 |
| user_001 | Jumping | 1.44604723026e12 | -1.95374 | -0.114362 | -0.268841 |
| user_001 | Jumping | 1.446047230325e12 | -0.229323 | -1.95374 | 2.75846 |
| user_001 | Jumping | 1.44604723039e12 | 2.68302 | 0.268841 | 0.535886 |
| user_001 | Jumping | 1.446047230455e12 | 1.15021 | -1.41725 | 0.880768 |
| user_001 | Jumping | 1.446047230519e12 | 2.98958 | -11.53382 | -0.920286 |
| user_001 | Jumping | 1.446047230584e12 | 8.00954 | -19.58108 | -0.1922 |
| user_001 | Jumping | 1.446047230649e12 | 7.31978 | -19.58108 | 2.52854 |
| user_001 | Jumping | 1.446047230713e12 | 5.44208 | -13.56479 | -0.498763 |
| user_001 | Jumping | 1.446047230779e12 | 0.728685 | -0.420925 | 1.68549 |
| user_001 | Jumping | 1.446047230842e12 | 1.18853 | 1.41845 | -2.22318 |
| user_001 | Jumping | 1.446047230907e12 | 0.15388 | -1.80046 | 2.03038 |
| user_001 | Jumping | 1.446047230972e12 | -3.7722e-2 | 1.07357 | -0.958607 |
| user_001 | Jumping | 1.446047231038e12 | 0.230521 | 0.728685 | -0.690364 |
| user_001 | Jumping | 1.446047231102e12 | 0.805325 | -1.14901 | 1.53221 |
| user_001 | Jumping | 1.446047231167e12 | 5.17384 | -9.15796 | -2.91294 |
| user_001 | Jumping | 1.446047231231e12 | 8.00954 | -19.54276 | 1.49389 |
| user_001 | Jumping | 1.446047231296e12 | 11.61165 | -19.58108 | 4.25296 |
| user_001 | Jumping | 1.446047231361e12 | 7.89458 | -18.35483 | 3.71647 |
| user_001 | Jumping | 1.446047231426e12 | 0.537083 | -3.21831 | 0.420925 |
| user_001 | Jumping | 1.44604723149e12 | -1.91542 | 4.10087 | -2.6447 |
| user_001 | Jumping | 1.446047231555e12 | -0.919089 | -0.382604 | 0.114362 |
| user_001 | Jumping | 1.44604723162e12 | 0.613724 | -0.842448 | 1.57053 |
| user_001 | Jumping | 1.446047231684e12 | 1.38013 | -0.459245 | 0.305964 |
| user_001 | Jumping | 1.446047231749e12 | 2.29982 | -1.49389 | -1.26517 |
| user_001 | Jumping | 1.446047231814e12 | 4.94392 | -10.95901 | -0.498763 |
| user_001 | Jumping | 1.446047231878e12 | 3.75599 | -19.27452 | -1.76333 |
| user_001 | Jumping | 1.446047231944e12 | 7.70298 | -19.58108 | -2.95126 |
| user_001 | Jumping | 1.446047232008e12 | 6.55337 | -17.51178 | -1.91661 |
| user_001 | Jumping | 1.446047232073e12 | 2.10822 | -2.03038 | -0.268841 |
| user_001 | Jumping | 1.446047232138e12 | -1.68549 | 3.67935 | -0.881966 |
| user_001 | Jumping | 1.446047232203e12 | 0.15388 | -0.114362 | 7.6042e-2 |
| user_001 | Jumping | 1.446047232267e12 | 2.79798 | -1.95374 | 0.689167 |
| user_001 | Jumping | 1.446047232332e12 | 1.45677 | -0.957409 | 0.420925 |
| user_001 | Jumping | 1.446047232397e12 | 1.18853 | -2.95007 | 0.650847 |
| user_001 | Jumping | 1.446047232462e12 | 3.44943 | -13.87135 | -3.71767 |
| user_001 | Jumping | 1.446047232526e12 | 3.79431 | -19.58108 | -0.345482 |
| user_001 | Jumping | 1.446047232591e12 | 6.09353 | -19.58108 | 2.87342 |
| user_001 | Jumping | 1.446047232655e12 | 4.48408 | -14.3312 | -0.498763 |
| user_001 | Jumping | 1.44604723272e12 | -3.7722e-2 | -1.68549 | 2.6435 |
| user_001 | Jumping | 1.446047232785e12 | 0.613724 | 2.68302 | -2.37646 |
| user_001 | Jumping | 1.44604723285e12 | 0.996927 | -0.497565 | 0.497565 |
| user_001 | Jumping | 1.446047232915e12 | 0.15388 | -0.689167 | 1.34061 |
| user_001 | Jumping | 1.446047232979e12 | 1.07357 | 1.07357 | -2.60638 |
| user_001 | Jumping | 1.446047233044e12 | 2.37646 | -3.29495 | -1.45677 |
| user_001 | Jumping | 1.446047233109e12 | 2.22318 | -16.09393 | 1.37893 |
| user_001 | Jumping | 1.446047233173e12 | 6.82161 | -19.58108 | 3.7722e-2 |
| user_001 | Jumping | 1.446047233238e12 | 8.39275 | -19.54276 | 1.22565 |
| user_001 | Jumping | 1.446047233303e12 | 1.22685 | -9.50284 | -0.307161 |
| user_001 | Jumping | 1.446047233368e12 | 1.83997 | -0.152682 | 1.41725 |
| user_001 | Jumping | 1.446047233432e12 | -0.765807 | 0.996927 | -1.91661 |
| user_001 | Jumping | 1.446047233497e12 | 0.728685 | -0.612526 | 0.152682 |
| user_001 | Jumping | 1.446047233562e12 | 0.996927 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.446047233626e12 | 1.22685 | -0.305964 | 3.7722e-2 |
| user_001 | Jumping | 1.446047233692e12 | 1.91661 | -2.95007 | -0.345482 |
| user_001 | Jumping | 1.446047233755e12 | 4.75232 | -14.63776 | -3.67935 |
| user_001 | Jumping | 1.446047233821e12 | 4.56072 | -19.58108 | -2.6447 |
| user_001 | Jumping | 1.446047233886e12 | 10.69197 | -19.38948 | -5.94025 |
| user_001 | Jumping | 1.44604723395e12 | 3.10454 | -10.42253 | -2.22318 |
| user_001 | Jumping | 1.446047234015e12 | -0.152682 | 1.61005 | 1.80046 |
| user_001 | Jumping | 1.44604723408e12 | -0.267643 | 1.83997 | -0.958607 |
| user_001 | Jumping | 1.446047234145e12 | 1.99325 | -0.842448 | -0.230521 |
| user_001 | Jumping | 1.44604723421e12 | 2.79798 | -0.114362 | 0.919089 |
| user_001 | Jumping | 1.446047234273e12 | 1.11189 | -0.152682 | 1.83878 |
| user_001 | Jumping | 1.446047234339e12 | 2.75966 | -5.05768 | -0.537083 |
| user_001 | Jumping | 1.446047234403e12 | 4.33079 | -16.82202 | -3.06622 |
| user_001 | Jumping | 1.446047234468e12 | 6.89825 | -19.58108 | -4.25415 |
| user_001 | Jumping | 1.446047234533e12 | 11.49669 | -19.50444 | -2.52974 |
| user_001 | Jumping | 1.446047234598e12 | 6.20849 | -9.08132 | -1.80165 |
| user_001 | Jumping | 1.446047234663e12 | 0.805325 | 1.15021 | -0.15388 |
| user_001 | Jumping | 1.446047234726e12 | -1.76214 | 2.14654 | -0.460442 |
| user_001 | Jumping | 1.446047234792e12 | 5.99e-4 | -1.11069 | -0.230521 |
| user_001 | Jumping | 1.446047234857e12 | 0.652044 | 0.230521 | -0.920286 |
| user_001 | Jumping | 1.446047234922e12 | 1.26517 | -0.114362 | -5.99e-4 |
| user_001 | Jumping | 1.446047234986e12 | 5.36544 | -4.09967 | -0.537083 |
| user_001 | Jumping | 1.446047235051e12 | 6.51505 | -17.05194 | -3.56439 |
| user_001 | Jumping | 1.446047235115e12 | 6.93658 | -19.58108 | -2.75966 |
| user_001 | Jumping | 1.446047235181e12 | 7.89458 | -19.58108 | -3.18118 |
| user_001 | Jumping | 1.446047235245e12 | 1.95493 | -9.6178 | -1.34181 |
| user_001 | Jumping | 1.44604723531e12 | -2.18366 | 3.14286 | -0.1922 |
| user_001 | Jumping | 1.446047235375e12 | -2.87342 | 1.99325 | -1.76333 |
| user_001 | Jumping | 1.446047235439e12 | 1.03525 | -1.80046 | 1.64717 |
| user_001 | Jumping | 1.446047235504e12 | 1.95493 | -0.689167 | 1.49389 |
| user_001 | Jumping | 1.446047235569e12 | 1.26517 | 0.383802 | -0.307161 |
| user_001 | Jumping | 1.446047235633e12 | 1.18853 | -2.72014 | 1.41725 |
| user_001 | Jumping | 1.446047235698e12 | 5.71033 | -16.51545 | -2.03158 |
| user_001 | Jumping | 1.446047235763e12 | 6.55337 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047235828e12 | 10.577 | -19.4278 | -1.57173 |
| user_001 | Jumping | 1.446047235892e12 | 2.79798 | -9.88604 | -0.843646 |
| user_001 | Jumping | 1.446047235957e12 | 0.652044 | -3.7722e-2 | 1.14901 |
| user_001 | Jumping | 1.446047236022e12 | 0.843646 | 1.95493 | -2.14654 |
| user_001 | Jumping | 1.446047236086e12 | 0.15388 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.446047236152e12 | 0.767005 | -0.114362 | 0.804128 |
| user_001 | Jumping | 1.446047236215e12 | -0.650847 | -0.650847 | -0.230521 |
| user_001 | Jumping | 1.446047236281e12 | 3.56439 | -5.90073 | 0.727487 |
| user_001 | Jumping | 1.446047236346e12 | 7.05154 | -17.97163 | -1.61005 |
| user_001 | Jumping | 1.44604723641e12 | 9.46572 | -19.58108 | 2.10702 |
| user_001 | Jumping | 1.446047236475e12 | 7.58802 | -18.54643 | 3.10335 |
| user_001 | Jumping | 1.44604723654e12 | 0.767005 | -6.28393 | 0.689167 |
| user_001 | Jumping | 1.446047236604e12 | 0.307161 | 2.29982 | -0.498763 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 |
| user_001 | Jumping | 1.446047236734e12 | 2.2615 | -1.45557 | 2.14534 |
| user_001 | Jumping | 1.446047236798e12 | 1.80165 | 0.345482 | 0.765807 |
| user_001 | Jumping | 1.446047236863e12 | -0.650847 | -0.305964 | -1.15021 |
| user_001 | Jumping | 1.446047236928e12 | 3.48775 | -7.31858 | -0.728685 |
| user_001 | Jumping | 1.446047236992e12 | 5.32712 | -18.92964 | 0.114362 |
| user_001 | Jumping | 1.446047237057e12 | 10.34708 | -19.58108 | 2.37526 |
| user_001 | Jumping | 1.446047237122e12 | 5.97857 | -17.78002 | 0.344284 |
| user_001 | Jumping | 1.446047237187e12 | 2.8363 | -3.25663 | 0.152682 |
| user_001 | Jumping | 1.446047237252e12 | -0.995729 | 3.37279 | -1.64837 |
| user_001 | Jumping | 1.446047237317e12 | -1.64717 | -0.650847 | 1.41725 |
| user_001 | Jumping | 1.446047237381e12 | 1.30349 | -0.152682 | 1.45557 |
| user_001 | Jumping | 1.446047237446e12 | 2.79798 | 5.99e-4 | -0.460442 |
| user_001 | Jumping | 1.44604723751e12 | 1.41845 | -0.459245 | -1.38013 |
| user_001 | Jumping | 1.446047237575e12 | 3.90927 | -6.62882 | -1.49509 |
| user_001 | Jumping | 1.44604723764e12 | 4.98224 | -19.27452 | 1.57053 |
| user_001 | Jumping | 1.446047237705e12 | 12.22478 | -19.58108 | 2.0687 |
| user_001 | Jumping | 1.446047237769e12 | 4.5224 | -16.74538 | -0.383802 |
| user_001 | Jumping | 1.446047237834e12 | 0.690364 | -0.880768 | -0.537083 |
| user_001 | Jumping | 1.446047237899e12 | 1.26517 | 2.18486 | -1.03525 |
| user_001 | Jumping | 1.446047237964e12 | 2.6447 | -3.7722e-2 | 1.18733 |
| user_001 | Jumping | 1.446047238028e12 | 2.56806 | -0.114362 | 1.03405 |
| user_001 | Jumping | 1.446047238094e12 | -1.14901 | 0.11556 | -0.307161 |
| user_001 | Jumping | 1.446047238157e12 | -0.574206 | -0.114362 | -0.920286 |
| user_001 | Jumping | 1.446047238223e12 | 2.60638 | -3.40991 | -0.422122 |
| user_001 | Jumping | 1.446047238287e12 | 6.32345 | -17.62674 | -0.728685 |
| user_001 | Jumping | 1.446047238352e12 | 10.53868 | -19.58108 | 0.919089 |
| user_001 | Jumping | 1.446047238417e12 | 7.85626 | -18.20155 | 0.995729 |
| user_001 | Jumping | 1.446047238481e12 | -0.152682 | -1.41725 | -1.22685 |
| user_001 | Jumping | 1.446047238546e12 | 1.26517 | 2.22318 | -1.87829 |
| user_001 | Jumping | 1.446047238611e12 | 0.537083 | -0.497565 | 1.76214 |
| user_001 | Jumping | 1.446047238676e12 | 1.95493 | 0.230521 | 1.60885 |
| user_001 | Jumping | 1.446047238741e12 | 0.920286 | 0.537083 | -0.422122 |
| user_001 | Jumping | 1.446047238806e12 | 5.99e-4 | -0.650847 | 0.497565 |
| user_001 | Jumping | 1.44604723887e12 | 1.64837 | -1.99206 | -1.72501 |
| user_001 | Jumping | 1.446047238935e12 | 5.0972 | -14.75272 | -1.41845 |
| user_001 | Jumping | 1.446047239e12 | 10.50036 | -19.58108 | 1.91542 |
| user_001 | Jumping | 1.446047239064e12 | 8.16283 | -19.58108 | 1.95374 |
| user_001 | Jumping | 1.446047239129e12 | 3.56439 | -9.4262 | -1.22685 |
| user_001 | Jumping | 1.446047239194e12 | 0.920286 | 2.79798 | -1.34181 |
| user_001 | Jumping | 1.446047239259e12 | 0.1922 | 0.652044 | -0.537083 |
| user_001 | Jumping | 1.446047239324e12 | 1.64837 | -0.957409 | 1.41725 |
| user_001 | Jumping | 1.446047239388e12 | 1.99325 | -0.191003 | 0.957409 |
| user_001 | Jumping | 1.446047239453e12 | 1.26517 | -0.305964 | -5.99e-4 |
| user_001 | Jumping | 1.446047239518e12 | 2.4531 | -4.17632 | -1.34181 |
| user_001 | Jumping | 1.446047239583e12 | 4.44576 | -16.05561 | -0.537083 |
| user_001 | Jumping | 1.446047239647e12 | 9.58068 | -19.58108 | 1.53221 |
| user_001 | Jumping | 1.446047239712e12 | 8.04786 | -19.2362 | 2.0687 |
| user_001 | Jumping | 1.446047239777e12 | 1.80165 | -6.01569 | -0.422122 |
| user_001 | Jumping | 1.446047239841e12 | 0.11556 | 2.10822 | -2.6447 |
| user_001 | Jumping | 1.446047239906e12 | 0.345482 | -0.612526 | -1.22685 |
| user_001 | Jumping | 1.446047239971e12 | 1.45677 | -0.880768 | 2.2603 |
| user_001 | Jumping | 1.446047240036e12 | 0.843646 | 1.11189 | -0.537083 |
| user_001 | Jumping | 1.4460472401e12 | 0.996927 | 3.8919e-2 | -0.575403 |
| user_001 | Jumping | 1.446047240166e12 | 3.29615 | -3.98471 | -0.422122 |
| user_001 | Jumping | 1.446047240231e12 | 5.21216 | -16.7837 | -0.613724 |
| user_001 | Jumping | 1.446047240295e12 | 7.39642 | -19.58108 | 1.11069 |
| user_001 | Jumping | 1.44604724036e12 | 10.23212 | -19.58108 | 0.459245 |
| user_001 | Jumping | 1.446047240424e12 | 1.80165 | -12.14694 | -1.22685 |
| user_001 | Jumping | 1.446047240489e12 | 0.575403 | 1.83997 | 3.7722e-2 |
| user_001 | Jumping | 1.446047240553e12 | -0.152682 | 2.22318 | -3.0279 |
| user_001 | Jumping | 1.446047240618e12 | 0.268841 | -1.91542 | 3.06503 |
| user_001 | Jumping | 1.446047240683e12 | 1.72501 | -0.535886 | 1.83878 |
| user_001 | Jumping | 1.446047240748e12 | -1.18733 | 0.996927 | -1.26517 |
| user_001 | Jumping | 1.446047240813e12 | 0.920286 | -0.535886 | -1.91661 |
| user_001 | Jumping | 1.446047240878e12 | 4.40743 | -11.61046 | 0.459245 |
| user_001 | Jumping | 1.446047240941e12 | 6.78329 | -19.58108 | 4.82776 |
| user_001 | Jumping | 1.446047241007e12 | 9.35075 | -19.58108 | 5.74745 |
| user_001 | Jumping | 1.446047241072e12 | 5.71033 | -15.74905 | -1.15021 |
| user_001 | Jumping | 1.446047241136e12 | -1.57053 | -1.80046 | -0.15388 |
| user_001 | Jumping | 1.446047241201e12 | -0.267643 | 2.18486 | -2.14654 |
| user_001 | Jumping | 1.446047241266e12 | 2.22318 | -1.80046 | 1.80046 |
| user_001 | Jumping | 1.44604724133e12 | -1.34061 | 0.767005 | -2.03158 |
| user_001 | Jumping | 1.446047241395e12 | -0.382604 | -0.842448 | 0.382604 |
| user_001 | Jumping | 1.446047241459e12 | -0.765807 | -1.41725 | 7.6042e-2 |
| user_001 | Jumping | 1.446047241525e12 | 5.97857 | -10.34589 | -1.49509 |
| user_001 | Jumping | 1.446047241589e12 | 11.34341 | -19.58108 | 6.13065 |
| user_001 | Jumping | 1.446047241654e12 | 10.15548 | -19.58108 | 8.58315 |
| user_001 | Jumping | 1.446047241718e12 | 4.67568 | -11.61046 | -0.307161 |
| user_001 | Jumping | 1.446047241783e12 | -1.37893 | -0.650847 | 0.459245 |
| user_001 | Jumping | 1.446047241849e12 | -0.919089 | 2.6447 | -3.14286 |
| user_001 | Jumping | 1.446047241913e12 | 1.07357 | -1.03405 | -0.613724 |
| user_001 | Jumping | 1.446047241977e12 | 0.15388 | -0.919089 | 2.18366 |
| user_001 | Jumping | 1.446047242042e12 | -0.995729 | 0.460442 | 0.305964 |
| user_001 | Jumping | 1.446047242107e12 | 2.03158 | -0.382604 | 7.6042e-2 |
| user_001 | Jumping | 1.446047242172e12 | 6.93658 | -10.84405 | -0.422122 |
| user_001 | Jumping | 1.446047242237e12 | 13.79591 | -19.58108 | 1.49389 |
| user_001 | Jumping | 1.446047242302e12 | 15.86521 | -19.50444 | -1.72501 |
| user_001 | Jumping | 1.446047242366e12 | 4.79064 | -12.03198 | 1.8771 |
| user_001 | Jumping | 1.446047242431e12 | -0.497565 | 1.45677 | -4.10087 |
| user_001 | Jumping | 1.446047242495e12 | 0.11556 | 0.613724 | -1.30349 |
| user_001 | Jumping | 1.44604724256e12 | -0.459245 | -0.305964 | 2.56686 |
| user_001 | Jumping | 1.446047242625e12 | 2.29982 | 1.18853 | -0.767005 |
| user_001 | Jumping | 1.44604724269e12 | 1.38013 | -0.305964 | -1.87829 |
| user_001 | Jumping | 1.446047242754e12 | 2.52974 | -2.75846 | 0.152682 |
| user_001 | Jumping | 1.446047242819e12 | 5.32712 | -13.21991 | -1.34181 |
| user_001 | Jumping | 1.446047242884e12 | 10.50036 | -19.58108 | 1.8771 |
| user_001 | Jumping | 1.446047242948e12 | 13.02951 | -19.58108 | -0.498763 |
| user_001 | Jumping | 1.446047243013e12 | 4.33079 | -10.34589 | -2.4531 |
| user_001 | Jumping | 1.446047243078e12 | 0.613724 | 0.498763 | 2.49022 |
| user_001 | Jumping | 1.446047243143e12 | 0.307161 | 1.53341 | -2.49142 |
| user_001 | Jumping | 1.446047243207e12 | 7.7239e-2 | -0.344284 | 1.91542 |
| user_001 | Jumping | 1.446047243272e12 | 2.03158 | 0.1922 | -5.99e-4 |
| user_001 | Jumping | 1.446047243337e12 | 1.22685 | 0.230521 | -1.87829 |
| user_001 | Jumping | 1.446047243401e12 | 0.537083 | -1.26397 | 0.535886 |
| user_001 | Jumping | 1.446047243467e12 | 5.2888 | -11.22725 | 0.689167 |
| user_001 | Jumping | 1.446047243531e12 | 11.57333 | -19.58108 | 0.344284 |
| user_001 | Jumping | 1.446047243596e12 | 14.524 | -19.58108 | 2.33694 |
| user_001 | Jumping | 1.446047243661e12 | 3.18118 | -11.8787 | 0.191003 |
| user_001 | Jumping | 1.446047243725e12 | -1.07237 | 1.15021 | 0.152682 |
| user_001 | Jumping | 1.44604724379e12 | 0.422122 | 1.95493 | -1.87829 |
| user_001 | Jumping | 1.446047243855e12 | -0.535886 | -0.957409 | 1.18733 |
| user_001 | Jumping | 1.44604724392e12 | 3.60271 | -0.114362 | 0.574206 |
| user_001 | Jumping | 1.446047243984e12 | 2.87462 | -0.535886 | -0.268841 |
| user_001 | Jumping | 1.446047244049e12 | 2.2615 | -1.53221 | -0.11556 |
| user_001 | Jumping | 1.446047244114e12 | 6.85994 | -10.61413 | -1.11189 |
| user_001 | Jumping | 1.446047244178e12 | 7.97122 | -19.58108 | -7.7239e-2 |
| user_001 | Jumping | 1.446047244243e12 | 14.86888 | -19.58108 | -1.41845 |
| user_001 | Jumping | 1.446047244308e12 | 8.50771 | -14.48448 | -0.613724 |
| user_001 | Jumping | 1.446047244373e12 | -0.535886 | -1.18733 | 1.64717 |
| user_001 | Jumping | 1.446047244437e12 | -4.5212 | 3.37279 | 0.191003 |
| user_001 | Jumping | 1.446047244502e12 | -1.72382 | 0.690364 | 0.191003 |
| user_003 | Jumping | 1.446047778034e12 | -2.75846 | -7.28026 | -1.45677 |
| user_003 | Jumping | 1.446047778099e12 | -3.75479 | -7.08866 | -2.37646 |
| user_003 | Jumping | 1.446047778164e12 | -2.14534 | -6.74378 | -2.22318 |
| user_003 | Jumping | 1.446047778229e12 | -2.22198 | -6.01569 | -2.2615 |
| user_003 | Jumping | 1.446047778293e12 | -3.63983 | -9.04299 | -2.6447 |
| user_003 | Jumping | 1.446047778358e12 | -4.82776 | -17.09026 | -4.02423 |
| user_003 | Jumping | 1.446047778422e12 | -5.82409 | -19.46612 | -6.70665 |
| user_003 | Jumping | 1.446047778488e12 | 0.805325 | -10.92069 | -1.49509 |
| user_003 | Jumping | 1.446047778552e12 | -4.67448 | 1.22685 | 1.64717 |
| user_003 | Jumping | 1.446047778616e12 | -1.37893 | 0.460442 | -2.18486 |
| user_003 | Jumping | 1.446047778682e12 | 0.1922 | -0.650847 | -7.7239e-2 |
| user_003 | Jumping | 1.446047778746e12 | -0.842448 | 0.11556 | -1.26517 |
| user_003 | Jumping | 1.446047778811e12 | -1.26397 | -11.91702 | -3.64103 |
| user_003 | Jumping | 1.446047778876e12 | -1.41725 | -19.58108 | -0.767005 |
| user_003 | Jumping | 1.44604777894e12 | -1.76214 | -15.44249 | -2.68302 |
| user_003 | Jumping | 1.446047779006e12 | -0.574206 | -9.00468 | -1.76333 |
| user_003 | Jumping | 1.446047779071e12 | -3.14167 | -4.36792 | -1.68669 |
| user_003 | Jumping | 1.446047779135e12 | -3.37159 | -4.3296 | -2.4531 |
| user_003 | Jumping | 1.4460477792e12 | -4.06135 | -4.75112 | -3.10454 |
| user_003 | Jumping | 1.446047779265e12 | -4.36792 | -11.26557 | -0.345482 |
| user_003 | Jumping | 1.446047779329e12 | -5.97737 | -19.12124 | -4.25415 |
| user_003 | Jumping | 1.446047779394e12 | -3.75479 | -19.19788 | -4.9056 |
| user_003 | Jumping | 1.446047779459e12 | -0.459245 | -7.39522 | -0.537083 |
| user_003 | Jumping | 1.446047779524e12 | -2.91174 | 3.10454 | -2.37646 |
| user_003 | Jumping | 1.446047779589e12 | -1.41725 | -0.995729 | -0.230521 |
| user_003 | Jumping | 1.446047779653e12 | 0.422122 | -0.114362 | 0.344284 |
| user_003 | Jumping | 1.446047779718e12 | -0.574206 | -0.535886 | -1.18853 |
| user_003 | Jumping | 1.446047779783e12 | -1.72382 | -13.98632 | -4.10087 |
| user_003 | Jumping | 1.446047779847e12 | -3.14167 | -19.58108 | -3.48775 |
| user_003 | Jumping | 1.446047779912e12 | -2.68182 | -13.21991 | -3.18118 |
| user_003 | Jumping | 1.446047779977e12 | -0.267643 | -8.92803 | -2.10822 |
| user_003 | Jumping | 1.446047780042e12 | -3.17999 | -3.98471 | -0.728685 |
| user_003 | Jumping | 1.446047780107e12 | -2.91174 | -4.48288 | -2.18486 |
| user_003 | Jumping | 1.446047780171e12 | -3.37159 | -5.78577 | -3.87095 |
| user_003 | Jumping | 1.446047780236e12 | -4.02303 | -9.84772 | -0.383802 |
| user_003 | Jumping | 1.446047780301e12 | -6.47553 | -17.81835 | -4.13919 |
| user_003 | Jumping | 1.446047780365e12 | -3.86975 | -19.58108 | -5.40376 |
| user_003 | Jumping | 1.44604778043e12 | -1.99206 | -14.44616 | -3.64103 |
| user_003 | Jumping | 1.446047780495e12 | -2.29862 | -1.18733 | -0.307161 |
| user_003 | Jumping | 1.446047780559e12 | -1.8771 | 1.99325 | -1.68669 |
| user_003 | Jumping | 1.446047780625e12 | 5.99e-4 | -0.344284 | 0.650847 |
| user_003 | Jumping | 1.446047780689e12 | -0.459245 | -0.191003 | -0.613724 |
| user_003 | Jumping | 1.446047780754e12 | -2.60518 | -6.9737 | -4.714 |
| user_003 | Jumping | 1.446047780818e12 | -1.41725 | -19.58108 | -2.56806 |
| user_003 | Jumping | 1.446047780884e12 | -3.40991 | -17.51178 | -4.59904 |
| user_003 | Jumping | 1.446047780948e12 | -1.95374 | -10.61413 | -2.91294 |
| user_003 | Jumping | 1.446047781013e12 | -3.06503 | -6.70546 | -1.76333 |
| user_003 | Jumping | 1.446047781078e12 | -2.6435 | -7.20362 | -1.87829 |
| user_003 | Jumping | 1.446047781143e12 | -2.60518 | -7.28026 | -1.64837 |
| user_003 | Jumping | 1.446047781208e12 | -1.72382 | -6.51385 | -2.0699 |
| user_003 | Jumping | 1.446047781272e12 | -0.689167 | -5.55585 | -2.37646 |
| user_003 | Jumping | 1.446047781337e12 | -3.67815 | -8.00835 | -1.99325 |
| user_003 | Jumping | 1.446047781402e12 | -5.63249 | -13.64143 | -3.44943 |
| user_003 | Jumping | 1.446047781467e12 | -5.97737 | -18.16323 | -5.59536 |
| user_003 | Jumping | 1.446047781531e12 | -2.03038 | -19.2362 | -5.25048 |
| user_003 | Jumping | 1.446047781596e12 | -2.14534 | -8.88971 | -1.18853 |
| user_003 | Jumping | 1.44604778166e12 | -3.06503 | 2.52974 | -0.767005 |
| user_003 | Jumping | 1.446047781725e12 | 0.230521 | 0.268841 | -0.345482 |
| user_003 | Jumping | 1.44604778179e12 | 0.15388 | -0.535886 | -0.690364 |
| user_003 | Jumping | 1.446047781855e12 | -0.420925 | -2.18366 | -2.68302 |
| user_003 | Jumping | 1.446047781919e12 | -3.63983 | -17.47346 | -4.44576 |
| user_003 | Jumping | 1.446047781985e12 | -2.91174 | -18.35483 | -4.714 |
| user_003 | Jumping | 1.446047782049e12 | -0.919089 | -10.88237 | -2.2615 |
| user_003 | Jumping | 1.446047782114e12 | -3.02671 | -6.93538 | -2.68302 |
| user_003 | Jumping | 1.446047782178e12 | -2.75846 | -7.3569 | -2.10822 |
| user_003 | Jumping | 1.446047782244e12 | -1.45557 | -7.85507 | -1.22685 |
| user_003 | Jumping | 1.446047782309e12 | -1.8771 | -6.32225 | -1.99325 |
| user_003 | Jumping | 1.446047782373e12 | -1.64717 | -5.90073 | -2.33814 |
| user_003 | Jumping | 1.446047782438e12 | -2.49022 | -8.08499 | -1.80165 |
| user_003 | Jumping | 1.446047782503e12 | -4.40624 | -11.26557 | -2.37646 |
| user_003 | Jumping | 1.446047782568e12 | -5.13432 | -17.1669 | -4.67568 |
| user_003 | Jumping | 1.446047782632e12 | -3.40991 | -19.54276 | -5.71033 |
| user_003 | Jumping | 1.446047782697e12 | -1.99206 | -13.48815 | -4.06255 |
| user_003 | Jumping | 1.446047782762e12 | -2.22198 | -1.68549 | 0.267643 |
| user_003 | Jumping | 1.446047782827e12 | -1.91542 | 1.80165 | -1.68669 |
| user_003 | Jumping | 1.446047782891e12 | 0.575403 | -0.152682 | 0.459245 |
| user_003 | Jumping | 1.446047782956e12 | -0.842448 | -0.804128 | -1.03525 |
| user_003 | Jumping | 1.446047783021e12 | -1.76214 | -12.22358 | -5.67201 |
| user_003 | Jumping | 1.446047783086e12 | -2.33694 | -19.19788 | -4.02423 |
| user_003 | Jumping | 1.446047783151e12 | -1.14901 | -14.98264 | -4.13919 |
| user_003 | Jumping | 1.446047783215e12 | -2.22198 | -9.04299 | -2.91294 |
| user_003 | Jumping | 1.44604778328e12 | -2.41358 | -5.59417 | -0.613724 |
| user_003 | Jumping | 1.446047783344e12 | -2.60518 | -5.78577 | -2.33814 |
| user_003 | Jumping | 1.446047783409e12 | -3.67815 | -4.44456 | -2.4531 |
| user_003 | Jumping | 1.446047783474e12 | -2.8351 | -4.63616 | -3.75599 |
| user_003 | Jumping | 1.446047783539e12 | -1.64717 | -16.4005 | -0.843646 |
| user_003 | Jumping | 1.446047783604e12 | -6.62882 | -19.58108 | -8.58435 |
| user_003 | Jumping | 1.446047783669e12 | -1.72382 | -14.67608 | -3.52607 |
| user_003 | Jumping | 1.446047783733e12 | -1.57053 | 1.34181 | 0.612526 |
| user_003 | Jumping | 1.446047783798e12 | -1.18733 | 1.22685 | -1.38013 |
| user_003 | Jumping | 1.446047783863e12 | -0.612526 | -0.612526 | -0.268841 |
| user_003 | Jumping | 1.446047783928e12 | -1.22565 | 0.345482 | -0.805325 |
| user_003 | Jumping | 1.446047783992e12 | -1.11069 | -2.2603 | -2.56806 |
| user_003 | Jumping | 1.446047784057e12 | -2.8351 | -17.62674 | -5.02056 |
| user_003 | Jumping | 1.446047784122e12 | -3.90807 | -19.19788 | -5.59536 |
| user_003 | Jumping | 1.446047784187e12 | -1.72382 | -12.6451 | -3.64103 |
| user_003 | Jumping | 1.446047784252e12 | -1.49389 | -6.7821 | -2.0699 |
| user_003 | Jumping | 1.446047784316e12 | -2.14534 | -4.67448 | -1.15021 |
| user_003 | Jumping | 1.446047784381e12 | -2.91174 | -6.93538 | -2.6447 |
| user_003 | Jumping | 1.446047784446e12 | -3.71647 | -5.86241 | -2.56806 |
| user_003 | Jumping | 1.44604778451e12 | -0.344284 | -6.51385 | -2.8363 |
| user_003 | Jumping | 1.446047784575e12 | -4.02303 | -14.98264 | -2.2615 |
| user_003 | Jumping | 1.446047784639e12 | -5.36425 | -19.58108 | -8.89091 |
| user_003 | Jumping | 1.446047784704e12 | -0.191003 | -14.906 | -3.94759 |
| user_003 | Jumping | 1.446047784769e12 | -2.4519 | 7.7239e-2 | 1.11069 |
| user_003 | Jumping | 1.446047784834e12 | -2.4519 | 1.22685 | -1.41845 |
| user_003 | Jumping | 1.446047784899e12 | -0.344284 | 0.345482 | -0.15388 |
| user_003 | Jumping | 1.446047784964e12 | -0.842448 | 0.422122 | -0.996927 |
| user_003 | Jumping | 1.446047785029e12 | -2.29862 | -4.3296 | -4.63736 |
| user_003 | Jumping | 1.446047785093e12 | -0.765807 | -18.92964 | -3.98591 |
| user_003 | Jumping | 1.446047785158e12 | -4.3296 | -19.38948 | -5.17384 |
| user_003 | Jumping | 1.446047785222e12 | -1.11069 | -12.6451 | -1.22685 |
| user_003 | Jumping | 1.446047785287e12 | -3.44823 | -7.24194 | -2.18486 |
| user_003 | Jumping | 1.446047785352e12 | -2.49022 | -6.51385 | -2.95126 |
| user_003 | Jumping | 1.446047785417e12 | -2.10702 | -6.85874 | -1.22685 |
| user_003 | Jumping | 1.446047785482e12 | -2.79678 | -7.05034 | -2.14654 |
| user_003 | Jumping | 1.446047785546e12 | -2.56686 | -5.01936 | -2.52974 |
| user_003 | Jumping | 1.446047785611e12 | -2.87342 | -6.62882 | -2.56806 |
| user_003 | Jumping | 1.446047785676e12 | -4.63616 | -14.44616 | -2.52974 |
| user_003 | Jumping | 1.446047785741e12 | -5.67081 | -19.58108 | -6.59169 |
| user_003 | Jumping | 1.446047785805e12 | -3.67815 | -17.66507 | -6.63001 |
| user_003 | Jumping | 1.44604778587e12 | -0.919089 | -4.82776 | -0.268841 |
| user_003 | Jumping | 1.446047785934e12 | -2.91174 | 2.87462 | -2.33814 |
| user_003 | Jumping | 1.446047785999e12 | -0.420925 | -0.420925 | 0.919089 |
| user_003 | Jumping | 1.446047786065e12 | -3.7722e-2 | 0.230521 | 0.382604 |
| user_003 | Jumping | 1.446047786129e12 | -0.842448 | -0.497565 | -3.0279 |
| user_003 | Jumping | 1.446047786194e12 | -0.995729 | -15.48081 | -4.33079 |
| user_003 | Jumping | 1.446047786259e12 | -3.98471 | -19.50444 | -6.89825 |
| user_003 | Jumping | 1.446047786324e12 | -0.574206 | -13.29655 | -3.94759 |
| user_003 | Jumping | 1.446047786388e12 | -1.41725 | -8.85139 | -1.38013 |
| user_003 | Jumping | 1.446047786453e12 | -3.94639 | -5.59417 | -3.14286 |
| user_003 | Jumping | 1.446047786518e12 | -2.18366 | -8.04667 | -3.06622 |
| user_003 | Jumping | 1.446047786582e12 | -1.60885 | -8.92803 | -1.15021 |
| user_003 | Jumping | 1.446047786647e12 | -2.72014 | -5.13432 | -2.33814 |
| user_003 | Jumping | 1.446047786712e12 | -2.03038 | -4.25296 | -3.18118 |
| user_003 | Jumping | 1.446047786777e12 | -3.48655 | -9.84772 | -1.45677 |
| user_003 | Jumping | 1.446047786841e12 | -6.01569 | -18.96796 | -6.78329 |
| user_003 | Jumping | 1.446047786906e12 | -4.44456 | -19.4278 | -8.62267 |
| user_003 | Jumping | 1.44604778697e12 | -0.267643 | -8.92803 | -1.57173 |
| user_003 | Jumping | 1.446047787035e12 | -2.91174 | 3.41111 | -1.53341 |
| user_003 | Jumping | 1.4460477871e12 | 0.268841 | -0.765807 | 0.689167 |
| user_003 | Jumping | 1.446047787165e12 | -0.650847 | 0.383802 | -0.728685 |
| user_003 | Jumping | 1.44604778723e12 | -0.880768 | 0.422122 | -1.03525 |
| user_003 | Jumping | 1.446047787294e12 | -1.60885 | -11.11229 | -5.4804 |
| user_003 | Jumping | 1.446047787358e12 | -4.59784 | -19.35116 | -5.94025 |
| user_003 | Jumping | 1.446047787424e12 | -2.41358 | -15.71073 | -5.74865 |
| user_003 | Jumping | 1.446047787489e12 | -1.26397 | -9.34956 | -3.06622 |
| user_003 | Jumping | 1.446047787553e12 | -3.44823 | -6.47553 | -2.75966 |
| user_003 | Jumping | 1.446047787618e12 | -2.4519 | -7.97003 | -2.2615 |
| user_003 | Jumping | 1.446047787683e12 | -1.95374 | -7.81675 | -1.83997 |
| user_003 | Jumping | 1.446047787747e12 | -2.75846 | -4.67448 | -3.06622 |
| user_003 | Jumping | 1.446047787812e12 | -2.22198 | -5.24928 | -2.0699 |
| user_003 | Jumping | 1.446047787877e12 | -3.29495 | -12.41518 | -1.49509 |
| user_003 | Jumping | 1.446047787942e12 | -5.44089 | -19.46612 | -6.20849 |
| user_003 | Jumping | 1.446047788007e12 | -3.79311 | -18.69972 | -7.70298 |
| user_003 | Jumping | 1.446047788071e12 | -0.382604 | -7.1653 | -1.83997 |
| user_003 | Jumping | 1.446047788136e12 | -3.14167 | 2.72134 | -0.575403 |
| user_003 | Jumping | 1.446047788201e12 | 0.460442 | -0.152682 | 0.114362 |
| user_003 | Jumping | 1.446047788265e12 | -0.842448 | 0.652044 | -0.230521 |
| user_003 | Jumping | 1.446047788331e12 | -2.2603 | -0.229323 | -1.45677 |
| user_003 | Jumping | 1.446047788395e12 | -1.45557 | -13.71807 | -6.63001 |
| user_003 | Jumping | 1.44604778846e12 | -3.14167 | -19.54276 | -6.32345 |
| user_003 | Jumping | 1.446047788524e12 | -1.83878 | -13.06663 | -4.40743 |
| user_003 | Jumping | 1.44604778859e12 | -1.83878 | -9.77108 | -3.25783 |
| user_003 | Jumping | 1.446047788654e12 | -3.14167 | -6.55217 | -2.6447 |
| user_003 | Jumping | 1.446047788719e12 | -2.10702 | -7.31858 | -2.10822 |
| user_003 | Jumping | 1.446047788784e12 | -2.14534 | -5.90073 | -2.0699 |
| user_003 | Jumping | 1.446047788848e12 | -1.57053 | -4.9044 | -2.75966 |
| user_003 | Jumping | 1.446047788913e12 | -2.75846 | -7.5485 | -1.76333 |
| user_003 | Jumping | 1.446047788978e12 | -4.98104 | -15.97897 | -4.67568 |
| user_003 | Jumping | 1.446047789043e12 | -3.10335 | -19.58108 | -8.16283 |
| user_003 | Jumping | 1.446047789107e12 | -1.68549 | -16.36218 | -5.25048 |
| user_003 | Jumping | 1.446047789172e12 | -2.52854 | -2.0687 | -0.345482 |
| user_003 | Jumping | 1.446047789236e12 | -1.72382 | 2.41478 | -2.37646 |
| user_003 | Jumping | 1.446047789301e12 | 0.843646 | -0.382604 | 1.11069 |
| user_003 | Jumping | 1.446047789366e12 | -0.689167 | 0.230521 | -0.728685 |
| user_003 | Jumping | 1.446047789431e12 | -1.14901 | -1.95374 | -2.72134 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 |
| user_003 | Jumping | 1.44604778956e12 | -3.29495 | -18.96796 | -5.05888 |
| user_003 | Jumping | 1.446047789625e12 | -2.22198 | -12.0703 | -3.98591 |
| user_003 | Jumping | 1.44604778969e12 | -2.79678 | -7.7401 | -2.37646 |
| user_003 | Jumping | 1.446047789755e12 | -1.95374 | -5.74745 | -2.22318 |
| user_003 | Jumping | 1.446047789818e12 | -2.03038 | -6.85874 | -1.68669 |
| user_003 | Jumping | 1.446047789883e12 | -2.91174 | -5.67081 | -2.52974 |
| user_003 | Jumping | 1.446047789949e12 | -1.49389 | -4.94272 | -2.75966 |
| user_003 | Jumping | 1.446047790013e12 | -3.37159 | -10.88237 | -1.57173 |
| user_003 | Jumping | 1.446047790078e12 | -5.51753 | -19.58108 | -7.24314 |
| user_003 | Jumping | 1.446047790143e12 | -4.48288 | -17.5501 | -6.9749 |
| user_003 | Jumping | 1.446047790207e12 | -0.612526 | -2.68182 | -0.613724 |
| user_003 | Jumping | 1.446047790272e12 | -1.8771 | 2.68302 | -2.03158 |
| user_003 | Jumping | 1.446047790337e12 | 1.15021 | -0.919089 | 1.64717 |
| user_003 | Jumping | 1.446047790402e12 | -2.03038 | 0.307161 | -1.22685 |
| user_003 | Jumping | 1.446047790467e12 | -1.34061 | -0.191003 | -1.61005 |
| user_003 | Jumping | 1.446047790531e12 | -3.02671 | -13.64143 | -5.17384 |
| user_003 | Jumping | 1.446047790596e12 | -4.75112 | -19.58108 | -7.62634 |
| user_003 | Jumping | 1.44604779066e12 | -2.60518 | -15.05928 | -6.01689 |
| user_003 | Jumping | 1.446047790726e12 | -1.26397 | -8.12331 | -3.10454 |
| user_003 | Jumping | 1.44604779079e12 | -3.25663 | -5.97737 | -3.0279 |
| user_003 | Jumping | 1.446047790855e12 | -0.420925 | -6.24561 | -2.2615 |
| user_003 | Jumping | 1.446047790919e12 | -1.83878 | -3.40991 | -3.60271 |
| user_003 | Jumping | 1.446047790984e12 | -3.94639 | -5.36425 | -3.75599 |
| user_003 | Jumping | 1.446047791049e12 | -2.2603 | -13.98632 | -3.67935 |
| user_003 | Jumping | 1.446047791114e12 | -7.70178 | -19.58108 | -7.5497 |
| user_003 | Jumping | 1.446047791179e12 | -2.52854 | -15.36585 | -6.66833 |
| user_003 | Jumping | 1.446047791243e12 | -0.574206 | -0.497565 | -0.230521 |
| user_003 | Jumping | 1.446047791308e12 | -2.0687 | 0.958607 | -0.575403 |
| user_003 | Jumping | 1.446047791373e12 | 0.11556 | -0.497565 | 0.344284 |
| user_003 | Jumping | 1.446047791438e12 | -1.30229 | 1.34181 | -1.11189 |
| user_003 | Jumping | 1.446047791503e12 | -0.382604 | -0.880768 | -2.22318 |
| user_003 | Jumping | 1.446047791568e12 | -2.87342 | -15.97897 | -6.28513 |
| user_003 | Jumping | 1.446047791632e12 | -2.95007 | -19.38948 | -7.51138 |
| user_003 | Jumping | 1.446047791697e12 | -4.59784 | -13.37319 | -5.02056 |
| user_003 | Jumping | 1.446047791762e12 | -2.14534 | -9.84772 | -3.2195 |
| user_003 | Jumping | 1.446047791827e12 | -2.29862 | -6.55217 | -2.49142 |
| user_003 | Jumping | 1.446047791891e12 | -1.68549 | -7.47186 | -2.29982 |
| user_003 | Jumping | 1.446047791956e12 | -1.22565 | -8.16163 | -2.56806 |
| user_003 | Jumping | 1.446047792021e12 | -2.95007 | -5.096 | -2.52974 |
| user_003 | Jumping | 1.446047792085e12 | -4.02303 | -4.06135 | -7.7413 |
| user_003 | Jumping | 1.446047792151e12 | -2.60518 | -14.40784 | 0.727487 |
| user_003 | Jumping | 1.446047792215e12 | -6.24561 | -19.58108 | -8.89091 |
| user_003 | Jumping | 1.44604779228e12 | -1.60885 | -12.56846 | -3.2195 |
| user_003 | Jumping | 1.446047792344e12 | -3.10335 | 3.14286 | -1.26517 |
| user_003 | Jumping | 1.44604779241e12 | -3.7722e-2 | 5.99e-4 | -0.11556 |
| user_003 | Jumping | 1.446047792474e12 | 0.268841 | -0.650847 | 0.114362 |
| user_003 | Jumping | 1.446047792539e12 | -2.68182 | 0.996927 | -1.99325 |
| user_003 | Jumping | 1.446047792603e12 | -1.64717 | -4.67448 | -5.51872 |
| user_003 | Jumping | 1.446047792669e12 | -2.87342 | -18.96796 | -5.86361 |
| user_003 | Jumping | 1.446047792733e12 | -3.29495 | -18.66139 | -4.44576 |
| user_003 | Jumping | 1.446047792798e12 | -4.02303 | -13.21991 | -4.79064 |
| user_003 | Jumping | 1.446047792863e12 | -2.95007 | -7.97003 | -2.79798 |
| user_003 | Jumping | 1.446047792928e12 | -3.17999 | -6.20729 | -2.29982 |
| user_003 | Jumping | 1.446047792992e12 | -3.25663 | -5.40257 | -2.37646 |
| user_003 | Jumping | 1.446047793057e12 | -3.40991 | -4.5212 | -3.18118 |
| user_003 | Jumping | 1.446047793122e12 | -3.29495 | -3.90807 | -3.94759 |
| user_003 | Jumping | 1.446047793186e12 | -4.7128 | -14.7144 | -2.75966 |
| user_003 | Jumping | 1.446047793251e12 | -4.55952 | -19.58108 | -7.77962 |
| user_003 | Jumping | 1.446047793316e12 | -2.95007 | -16.32385 | -6.93658 |
| user_003 | Jumping | 1.44604779338e12 | -1.26397 | -2.14534 | -1.38013 |
| user_003 | Jumping | 1.446047793445e12 | -2.10702 | 2.33814 | -1.64837 |
| user_003 | Jumping | 1.44604779351e12 | -0.420925 | -0.267643 | 1.07237 |
| user_003 | Jumping | 1.446047793575e12 | -0.995729 | 0.383802 | -0.537083 |
| user_003 | Jumping | 1.44604779364e12 | -1.14901 | -1.76214 | -2.29982 |
| user_003 | Jumping | 1.446047793704e12 | -2.56686 | -14.82936 | -6.28513 |
| user_003 | Jumping | 1.446047793768e12 | -3.40991 | -19.31284 | -7.3581 |
| user_003 | Jumping | 1.446047793834e12 | -3.52487 | -13.87135 | -5.63368 |
| user_003 | Jumping | 1.446047793899e12 | -2.68182 | -10.84405 | -3.10454 |
| user_003 | Jumping | 1.446047793963e12 | -2.72014 | -4.78944 | -2.72134 |
| user_003 | Jumping | 1.446047794028e12 | -1.03405 | -4.94272 | -1.68669 |
| user_003 | Jumping | 1.446047794093e12 | -2.10702 | -5.93905 | -2.14654 |
| user_003 | Jumping | 1.446047794158e12 | -1.57053 | -5.90073 | -3.2195 |
| user_003 | Jumping | 1.446047794222e12 | -4.3296 | -8.08499 | -1.15021 |
| user_003 | Jumping | 1.446047794287e12 | -4.48288 | -19.08292 | -6.36177 |
| user_003 | Jumping | 1.446047794352e12 | -4.67448 | -19.58108 | -8.27779 |
| user_003 | Jumping | 1.446047794416e12 | 5.99e-4 | -10.76741 | -2.33814 |
| user_003 | Jumping | 1.446047794481e12 | -1.95374 | 3.41111 | -1.07357 |
| user_003 | Jumping | 1.446047794546e12 | -0.152682 | -0.689167 | 0.229323 |
| user_003 | Jumping | 1.446047794611e12 | -0.957409 | 0.422122 | -0.460442 |
| user_003 | Jumping | 1.446047794676e12 | -0.957409 | 0.996927 | -0.881966 |
| user_003 | Jumping | 1.44604779474e12 | -1.57053 | -3.60151 | -4.75232 |
| user_003 | Jumping | 1.446047794805e12 | -1.99206 | -18.35483 | -5.78697 |
| user_003 | Jumping | 1.44604779487e12 | -3.71647 | -19.2362 | -6.47673 |
| user_003 | Jumping | 1.446047794935e12 | -0.842448 | -14.94432 | -4.17751 |
| user_002 | Standing | 1.446309439342e12 | -2.49022 | -9.19628 | -1.11189 |
| user_002 | Standing | 1.446309439349e12 | -2.41358 | -9.15796 | -1.22685 |
| user_002 | Standing | 1.446309439358e12 | -2.56686 | -9.15796 | -1.30349 |
| user_002 | Standing | 1.446309439365e12 | -2.75846 | -9.08132 | -1.11189 |
| user_002 | Standing | 1.446309439373e12 | -2.75846 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309439382e12 | -2.8351 | -9.15796 | -0.920286 |
| user_002 | Standing | 1.44630943939e12 | -2.8357 | -9.15855 | -0.919687 |
| user_002 | Standing | 1.446309439398e12 | -2.95007 | -9.2346 | -0.881966 |
| user_002 | Standing | 1.446309439406e12 | -2.8351 | -9.11964 | -0.958607 |
| user_002 | Standing | 1.446309439414e12 | -2.8351 | -8.96635 | -1.15021 |
| user_002 | Standing | 1.446309439422e12 | -2.95007 | -9.08132 | -0.920286 |
| user_002 | Standing | 1.44630943943e12 | -2.95007 | -8.88971 | -0.996927 |
| user_002 | Standing | 1.446309439438e12 | -2.91174 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439447e12 | -3.02671 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309439454e12 | -2.79678 | -8.96635 | -1.15021 |
| user_002 | Standing | 1.446309439463e12 | -2.87342 | -9.08132 | -1.34181 |
| user_002 | Standing | 1.446309439471e12 | -2.98839 | -9.19628 | -1.22685 |
| user_002 | Standing | 1.446309439479e12 | -2.95007 | -9.00468 | -1.30349 |
| user_002 | Standing | 1.446309439487e12 | -2.98839 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439495e12 | -3.06503 | -9.00468 | -0.958607 |
| user_002 | Standing | 1.446309439503e12 | -3.02671 | -9.04299 | -0.881966 |
| user_002 | Standing | 1.446309439511e12 | -2.91174 | -9.00468 | -0.920286 |
| user_002 | Standing | 1.446309439519e12 | -2.95007 | -9.08132 | -0.767005 |
| user_002 | Standing | 1.446309439527e12 | -2.8351 | -9.00468 | -0.958607 |
| user_002 | Standing | 1.446309439536e12 | -2.95007 | -9.00468 | -0.996927 |
| user_002 | Standing | 1.446309439543e12 | -2.75846 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309439552e12 | -2.6435 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.44630943956e12 | -2.72014 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439568e12 | -2.68182 | -9.04299 | -1.11189 |
| user_002 | Standing | 1.446309439576e12 | -2.60518 | -9.00468 | -1.11189 |
| user_002 | Standing | 1.446309439584e12 | -2.52854 | -8.88971 | -1.15021 |
| user_002 | Standing | 1.446309439593e12 | -2.6435 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309439601e12 | -2.60518 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309439609e12 | -2.6435 | -9.04299 | -0.996927 |
| user_002 | Standing | 1.446309439616e12 | -2.75846 | -9.04299 | -0.920286 |
| user_002 | Standing | 1.446309439624e12 | -2.6435 | -9.19628 | -0.805325 |
| user_002 | Standing | 1.446309439633e12 | -2.79678 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309439641e12 | -2.87342 | -8.92803 | -0.958607 |
| user_002 | Standing | 1.446309439649e12 | -2.60518 | -9.11964 | -0.843646 |
| user_002 | Standing | 1.446309439657e12 | -2.8351 | -9.08132 | -0.843646 |
| user_002 | Standing | 1.446309439665e12 | -2.8351 | -9.2346 | -0.920286 |
| user_002 | Standing | 1.446309439674e12 | -2.72014 | -9.11964 | -0.958607 |
| user_002 | Standing | 1.446309439682e12 | -2.87342 | -9.15796 | -1.18853 |
| user_002 | Standing | 1.44630943969e12 | -3.06503 | -9.2346 | -1.18853 |
| user_002 | Standing | 1.446309439698e12 | -2.8351 | -9.08132 | -0.996927 |
| user_002 | Standing | 1.446309439705e12 | -2.98839 | -9.04299 | -1.03525 |
| user_002 | Standing | 1.446309439713e12 | -3.17999 | -8.96635 | -1.18853 |
| user_002 | Standing | 1.446309439722e12 | -3.21831 | -8.88971 | -1.18853 |
| user_002 | Standing | 1.446309439729e12 | -3.17999 | -8.92803 | -1.15021 |
| user_002 | Standing | 1.446309439738e12 | -3.17999 | -8.77475 | -1.22685 |
| user_002 | Standing | 1.446309439746e12 | -3.33327 | -8.65979 | -1.07357 |
| user_002 | Standing | 1.446309439754e12 | -3.37159 | -8.58315 | -1.34181 |
| user_002 | Standing | 1.446309439762e12 | -3.33327 | -8.77475 | -1.18853 |
| user_002 | Standing | 1.446309439771e12 | -3.33327 | -8.58315 | -1.07357 |
| user_002 | Standing | 1.446309439779e12 | -3.14167 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309439787e12 | -3.25663 | -8.85139 | -0.881966 |
| user_002 | Standing | 1.446309439795e12 | -3.10335 | -8.85139 | -0.920286 |
| user_002 | Standing | 1.446309439803e12 | -2.87342 | -8.85139 | -0.843646 |
| user_002 | Standing | 1.446309439811e12 | -2.91174 | -8.92803 | -0.767005 |
| user_002 | Standing | 1.446309439819e12 | -2.87342 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309439827e12 | -2.98839 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309439835e12 | -2.79678 | -9.11964 | -1.07357 |
| user_002 | Standing | 1.446309439843e12 | -2.87342 | -9.08132 | -0.920286 |
| user_002 | Standing | 1.446309439851e12 | -2.95007 | -9.15796 | -0.805325 |
| user_002 | Standing | 1.446309439859e12 | -3.02671 | -8.85139 | -0.728685 |
| user_002 | Standing | 1.446309439868e12 | -2.98839 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309439875e12 | -2.98839 | -8.96635 | -0.690364 |
| user_002 | Standing | 1.446309439883e12 | -2.91174 | -8.96635 | -0.881966 |
| user_002 | Standing | 1.446309439892e12 | -3.02671 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.4463094399e12 | -3.06503 | -9.04299 | -0.767005 |
| user_002 | Standing | 1.446309439908e12 | -2.87342 | -8.73643 | -0.652044 |
| user_002 | Standing | 1.446309439916e12 | -2.95007 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309439924e12 | -3.10335 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309439932e12 | -2.91174 | -8.77475 | -0.652044 |
| user_002 | Standing | 1.44630943994e12 | -2.91174 | -9.04299 | -0.805325 |
| user_002 | Standing | 1.446309439948e12 | -2.91174 | -9.04299 | -0.767005 |
| user_002 | Standing | 1.446309439957e12 | -3.02671 | -9.04299 | -0.652044 |
| user_002 | Standing | 1.446309439965e12 | -3.06503 | -9.27292 | -0.805325 |
| user_002 | Standing | 1.446309439973e12 | -3.14167 | -9.00468 | -0.767005 |
| user_002 | Standing | 1.44630943998e12 | -2.95007 | -8.96635 | -0.843646 |
| user_002 | Standing | 1.446309439989e12 | -3.10335 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439997e12 | -2.98839 | -9.19628 | -1.03525 |
| user_002 | Standing | 1.446309440004e12 | -3.10335 | -9.19628 | -1.15021 |
| user_002 | Standing | 1.446309440013e12 | -2.91174 | -9.11964 | -1.18853 |
| user_002 | Standing | 1.446309440021e12 | -2.91174 | -9.08132 | -1.22685 |
| user_002 | Standing | 1.446309440029e12 | -2.95007 | -9.00468 | -1.26517 |
| user_002 | Standing | 1.446309440038e12 | -3.06503 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309440045e12 | -3.06503 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440054e12 | -3.10335 | -9.11964 | -1.07357 |
| user_002 | Standing | 1.446309440062e12 | -3.10335 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309440069e12 | -3.10335 | -8.96635 | -1.03525 |
| user_002 | Standing | 1.446309440078e12 | -3.17999 | -9.00468 | -1.11189 |
| user_002 | Standing | 1.446309440086e12 | -3.14167 | -8.85139 | -0.996927 |
| user_002 | Standing | 1.446309440094e12 | -3.17999 | -9.00468 | -0.728685 |
| user_002 | Standing | 1.446309440102e12 | -3.17999 | -8.96635 | -0.958607 |
| user_002 | Standing | 1.446309440111e12 | -3.10335 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440119e12 | -3.17999 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440126e12 | -3.02671 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440135e12 | -3.06503 | -9.00468 | -0.728685 |
| user_002 | Standing | 1.446309440142e12 | -3.21831 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309440151e12 | -3.33327 | -8.85139 | -0.767005 |
| user_002 | Standing | 1.446309440158e12 | -3.21831 | -9.04299 | -0.652044 |
| user_002 | Standing | 1.446309440167e12 | -3.33327 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440175e12 | -3.37159 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309440183e12 | -3.37159 | -9.00468 | -0.652044 |
| user_002 | Standing | 1.446309440192e12 | -3.33327 | -8.81307 | -0.460442 |
| user_002 | Standing | 1.446309440199e12 | -3.21831 | -9.08132 | -0.537083 |
| user_002 | Standing | 1.446309440208e12 | -3.21831 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309440215e12 | -3.14167 | -8.96635 | -0.575403 |
| user_002 | Standing | 1.446309440224e12 | -3.17999 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309440232e12 | -3.17999 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309440239e12 | -3.14167 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309440248e12 | -3.10335 | -9.04299 | -0.843646 |
| user_002 | Standing | 1.446309440255e12 | -3.14167 | -8.88971 | -0.767005 |
| user_002 | Standing | 1.446309440264e12 | -3.06503 | -9.11964 | -0.805325 |
| user_002 | Standing | 1.446309440272e12 | -3.14167 | -8.96635 | -0.690364 |
| user_002 | Standing | 1.44630944028e12 | -3.29495 | -9.08132 | -0.843646 |
| user_002 | Standing | 1.446309440288e12 | -3.33327 | -8.81307 | -0.575403 |
| user_002 | Standing | 1.446309440296e12 | -3.21831 | -8.88971 | -0.575403 |
| user_002 | Standing | 1.446309440305e12 | -3.21831 | -8.96635 | -0.613724 |
| user_002 | Standing | 1.446309440312e12 | -3.14167 | -8.77475 | -0.537083 |
| user_002 | Standing | 1.44630944032e12 | -3.10335 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440329e12 | -3.21831 | -9.00468 | -0.613724 |
| user_002 | Standing | 1.446309440337e12 | -3.14167 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440345e12 | -3.21831 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309440353e12 | -3.06503 | -8.92803 | -0.575403 |
| user_002 | Standing | 1.446309440361e12 | -3.25663 | -9.11964 | -0.728685 |
| user_002 | Standing | 1.446309440369e12 | -3.21831 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.446309440377e12 | -3.06503 | -8.92803 | -0.843646 |
| user_002 | Standing | 1.446309440385e12 | -3.06503 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309440394e12 | -3.21831 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440402e12 | -3.02671 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.44630944041e12 | -3.21831 | -8.96635 | -0.652044 |
| user_002 | Standing | 1.446309440417e12 | -3.25663 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309440426e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440434e12 | -3.37159 | -8.85139 | -0.767005 |
| user_002 | Standing | 1.446309440443e12 | -3.21831 | -8.85139 | -0.537083 |
| user_002 | Standing | 1.44630944045e12 | -3.10335 | -9.11964 | -0.767005 |
| user_002 | Standing | 1.446309440458e12 | -3.10335 | -9.19628 | -0.920286 |
| user_002 | Standing | 1.446309440467e12 | -3.10335 | -9.00468 | -0.767005 |
| user_002 | Standing | 1.446309440474e12 | -3.02671 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309440482e12 | -3.10335 | -8.92803 | -0.805325 |
| user_002 | Standing | 1.446309440491e12 | -3.17999 | -8.92803 | -0.843646 |
| user_002 | Standing | 1.446309440498e12 | -3.33327 | -8.96635 | -0.805325 |
| user_002 | Standing | 1.446309440506e12 | -3.29495 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440515e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440523e12 | -3.29495 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.446309440531e12 | -3.37159 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309440539e12 | -3.33327 | -8.92803 | -0.613724 |
| user_002 | Standing | 1.446309440547e12 | -3.33327 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440555e12 | -3.21831 | -9.08132 | -0.690364 |
| user_002 | Standing | 1.446309440564e12 | -3.06503 | -8.88971 | -0.652044 |
| user_002 | Standing | 1.446309440571e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.44630944058e12 | -3.06503 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440587e12 | -3.10335 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440596e12 | -3.06503 | -9.08132 | -0.613724 |
| user_002 | Standing | 1.446309440604e12 | -2.87342 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440612e12 | -2.95007 | -9.08132 | -0.728685 |
| user_002 | Standing | 1.44630944062e12 | -3.14167 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309440628e12 | -3.02671 | -8.92803 | -0.537083 |
| user_002 | Standing | 1.446309440636e12 | -3.14167 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309440645e12 | -3.10335 | -9.08132 | -0.383802 |
| user_002 | Standing | 1.446309440653e12 | -3.14167 | -8.81307 | -0.307161 |
| user_002 | Standing | 1.44630944066e12 | -3.17999 | -8.85139 | -0.422122 |
| user_002 | Standing | 1.446309440669e12 | -3.21831 | -8.85139 | -0.1922 |
| user_002 | Standing | 1.446309440676e12 | -3.17999 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309440685e12 | -3.02671 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309440693e12 | -3.21831 | -8.85139 | -0.498763 |
| user_002 | Standing | 1.446309440701e12 | -3.10335 | -9.08132 | -0.498763 |
| user_002 | Standing | 1.446309440709e12 | -3.29495 | -8.96635 | -0.537083 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309440725e12 | -3.10335 | -9.15796 | -0.498763 |
| user_002 | Standing | 1.446309440734e12 | -2.91174 | -9.00468 | -0.345482 |
| user_002 | Standing | 1.446309440741e12 | -3.17999 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440749e12 | -3.21831 | -9.08132 | -0.537083 |
| user_002 | Standing | 1.446309440757e12 | -3.06503 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440766e12 | -3.06503 | -9.00468 | -0.613724 |
| user_002 | Standing | 1.446309440774e12 | -3.10335 | -8.92803 | -0.498763 |
| user_002 | Standing | 1.446309440782e12 | -3.25663 | -8.92803 | -0.613724 |
| user_002 | Standing | 1.44630944079e12 | -3.17999 | -8.92803 | -0.728685 |
| user_002 | Standing | 1.446309440798e12 | -3.29495 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309440806e12 | -3.17999 | -9.08132 | -0.422122 |
| user_002 | Standing | 1.446309440814e12 | -3.21831 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440822e12 | -3.06503 | -8.96635 | -0.460442 |
| user_002 | Standing | 1.446309440831e12 | -3.17999 | -9.04299 | -0.537083 |
| user_002 | Standing | 1.446309440839e12 | -3.14167 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309440847e12 | -3.17999 | -9.08132 | -0.345482 |
| user_002 | Standing | 1.446309440855e12 | -3.21831 | -9.08132 | -0.613724 |
| user_002 | Standing | 1.446309440863e12 | -3.33327 | -9.15796 | -0.460442 |
| user_002 | Standing | 1.446309440871e12 | -3.37159 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440879e12 | -3.17999 | -8.92803 | -0.537083 |
| user_002 | Standing | 1.446309440887e12 | -3.17999 | -8.96635 | -0.345482 |
| user_002 | Standing | 1.446309440896e12 | -3.17999 | -9.04299 | -0.383802 |
| user_002 | Standing | 1.446309440904e12 | -3.37159 | -9.11964 | -0.345482 |
| user_002 | Standing | 1.446309440912e12 | -3.33327 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.44630944092e12 | -3.44823 | -8.96635 | -0.460442 |
| user_002 | Standing | 1.446309440927e12 | -3.25663 | -8.81307 | -0.345482 |
| user_002 | Standing | 1.446309440936e12 | -3.25663 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309440944e12 | -3.40991 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440951e12 | -3.21831 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.44630944096e12 | -3.33327 | -8.85139 | -0.498763 |
| user_002 | Standing | 1.446309440968e12 | -3.37159 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309440976e12 | -3.17999 | -8.81307 | -0.728685 |
| user_002 | Standing | 1.446309440985e12 | -2.98839 | -8.88971 | -0.575403 |
| user_002 | Standing | 1.446309440992e12 | -3.06503 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309441001e12 | -2.87342 | -8.88971 | -0.652044 |
| user_002 | Standing | 1.446309441009e12 | -3.06503 | -9.11964 | -0.805325 |
| user_002 | Standing | 1.446309441017e12 | -2.91174 | -9.08132 | -0.652044 |
| user_002 | Standing | 1.446309441025e12 | -2.87342 | -9.00468 | -0.498763 |
| user_002 | Standing | 1.446309441033e12 | -2.91174 | -9.15796 | -0.422122 |
| user_002 | Standing | 1.446309441041e12 | -2.91174 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441049e12 | -2.87342 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309441057e12 | -3.02671 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441065e12 | -3.02671 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309441073e12 | -3.06503 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309441081e12 | -2.95007 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.44630944109e12 | -3.06503 | -9.04299 | -0.613724 |
| user_002 | Standing | 1.446309441098e12 | -2.95007 | -9.11964 | -0.498763 |
| user_002 | Standing | 1.446309441106e12 | -2.98839 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441113e12 | -2.98839 | -9.11964 | -0.498763 |
| user_002 | Standing | 1.446309441121e12 | -2.98839 | -9.15796 | -0.613724 |
| user_002 | Standing | 1.44630944113e12 | -3.14167 | -9.08132 | -0.575403 |
| user_002 | Standing | 1.446309441138e12 | -3.10335 | -9.15796 | -0.613724 |
| user_002 | Standing | 1.446309441146e12 | -2.98839 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441154e12 | -3.17999 | -9.00468 | -0.537083 |
| user_002 | Standing | 1.446309441162e12 | -3.06503 | -8.85139 | -0.537083 |
| user_002 | Standing | 1.44630944117e12 | -3.25663 | -9.11964 | -0.460442 |
| user_002 | Standing | 1.446309441179e12 | -3.29495 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441186e12 | -3.33327 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441195e12 | -3.17999 | -8.96635 | -0.575403 |
| user_002 | Standing | 1.446309441202e12 | -3.21831 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441211e12 | -3.52487 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441219e12 | -3.40991 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309441226e12 | -3.37159 | -8.77475 | -0.498763 |
| user_002 | Standing | 1.446309441235e12 | -3.48655 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441243e12 | -3.29495 | -8.96635 | -0.652044 |
| user_002 | Standing | 1.446309441252e12 | -3.25663 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.44630944126e12 | -3.37159 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309441268e12 | -3.14167 | -8.85139 | -0.690364 |
| user_002 | Standing | 1.446309441276e12 | -3.29495 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441284e12 | -3.37159 | -9.11964 | -0.575403 |
| user_002 | Standing | 1.446309441292e12 | -3.33327 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.4463094413e12 | -3.25663 | -8.77475 | -0.460442 |
| user_002 | Standing | 1.446309441308e12 | -3.40991 | -8.65979 | -0.345482 |
| user_002 | Standing | 1.446309441316e12 | -3.17999 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309441324e12 | -3.44823 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309441332e12 | -3.25663 | -8.73643 | -0.383802 |
| user_002 | Standing | 1.446309441341e12 | -3.37159 | -9.08132 | -0.460442 |
| user_002 | Standing | 1.446309441349e12 | -3.44823 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441357e12 | -3.25663 | -8.92803 | -0.230521 |
| user_002 | Standing | 1.446309441365e12 | -3.25663 | -8.81307 | -0.383802 |
| user_002 | Standing | 1.446309441373e12 | -3.44823 | -8.96635 | -0.268841 |
| user_002 | Standing | 1.446309441381e12 | -3.40991 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441389e12 | -3.33327 | -8.92803 | -0.345482 |
| user_002 | Standing | 1.446309441397e12 | -3.52487 | -8.73643 | -0.307161 |
| user_002 | Standing | 1.446309441405e12 | -3.40991 | -8.81307 | -0.1922 |
| user_002 | Standing | 1.446309441413e12 | -3.29495 | -9.00468 | -0.307161 |
| user_002 | Standing | 1.446309441421e12 | -3.33327 | -9.04299 | -0.1922 |
| user_002 | Standing | 1.44630944143e12 | -3.29495 | -8.88971 | -0.230521 |
| user_002 | Standing | 1.446309441437e12 | -3.17999 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441445e12 | -3.37159 | -8.85139 | -0.422122 |
| user_002 | Standing | 1.446309441453e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441462e12 | -3.44823 | -8.85139 | -0.383802 |
| user_002 | Standing | 1.44630944147e12 | -3.25663 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441478e12 | -3.37159 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309441486e12 | -3.29495 | -8.88971 | -0.345482 |
| user_002 | Standing | 1.446309441494e12 | -3.17999 | -8.92803 | -0.345482 |
| user_002 | Standing | 1.446309441501e12 | -3.29495 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.44630944151e12 | -3.14167 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441518e12 | -3.17999 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441527e12 | -3.06503 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441535e12 | -3.33327 | -8.81307 | -0.613724 |
| user_002 | Standing | 1.446309441543e12 | -3.10335 | -8.92803 | -0.383802 |
| user_002 | Standing | 1.446309441551e12 | -3.29495 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309441558e12 | -3.21831 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441567e12 | -3.25663 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441575e12 | -3.17999 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441583e12 | -3.21831 | -8.77475 | -0.422122 |
| user_002 | Standing | 1.446309441591e12 | -3.40991 | -8.73643 | -0.230521 |
| user_002 | Standing | 1.446309441599e12 | -3.44823 | -8.92803 | -0.498763 |
| user_002 | Standing | 1.446309441607e12 | -3.25663 | -8.81307 | -0.383802 |
| user_002 | Standing | 1.446309441615e12 | -3.40991 | -8.77475 | -0.345482 |
| user_002 | Standing | 1.446309441623e12 | -3.29495 | -8.69811 | -0.307161 |
| user_002 | Standing | 1.446309441632e12 | -3.25663 | -8.65979 | -0.460442 |
| user_002 | Standing | 1.446309441639e12 | -3.21831 | -8.77475 | -0.422122 |
| user_002 | Standing | 1.446309441648e12 | -3.17999 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309441655e12 | -3.06503 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441664e12 | -3.17999 | -9.2346 | -0.460442 |
| user_002 | Standing | 1.446309441672e12 | -3.10335 | -9.11964 | -0.422122 |
| user_002 | Standing | 1.44630944168e12 | -3.10335 | -9.19628 | -0.345482 |
| user_002 | Standing | 1.446309441688e12 | -3.29495 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441696e12 | -3.21831 | -8.96635 | -0.307161 |
| user_002 | Standing | 1.446309441705e12 | -3.14167 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441713e12 | -3.25663 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309441721e12 | -3.25663 | -9.00468 | -0.575403 |
| user_002 | Standing | 1.446309441729e12 | -3.29495 | -9.04299 | -0.345482 |
| user_002 | Standing | 1.446309441737e12 | -3.25663 | -9.00468 | -0.575403 |
| user_002 | Standing | 1.446309441745e12 | -3.17999 | -8.96635 | -0.537083 |
| user_002 | Standing | 1.446309441752e12 | -3.25663 | -9.11964 | -0.422122 |
| user_002 | Standing | 1.446309441761e12 | -3.21831 | -9.11964 | -0.307161 |
| user_002 | Standing | 1.446309441769e12 | -3.10335 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441777e12 | -3.10335 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441785e12 | -3.21831 | -9.11964 | -0.345482 |
| user_002 | Standing | 1.446309441793e12 | -3.17999 | -9.2346 | -0.307161 |
| user_002 | Standing | 1.446309441802e12 | -3.17999 | -9.08132 | -0.383802 |
| user_002 | Standing | 1.446309441809e12 | -3.17999 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309441818e12 | -3.06503 | -9.04299 | -0.383802 |
| user_002 | Standing | 1.446309441825e12 | -3.14167 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441833e12 | -3.25663 | -8.88971 | -0.422122 |
| user_002 | Standing | 1.446309441842e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.44630944185e12 | -3.21831 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441858e12 | -3.02671 | -8.81307 | -0.460442 |
| user_002 | Standing | 1.446309441867e12 | -3.17999 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441875e12 | -3.06503 | -8.85139 | -0.268841 |
| user_002 | Standing | 1.446309441882e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441891e12 | -3.33327 | -8.88971 | -0.268841 |
| user_002 | Standing | 1.446309441899e12 | -3.33327 | -8.81307 | -0.1922 |
| user_002 | Standing | 1.446309441907e12 | -3.17999 | -8.77475 | -0.230521 |
| user_002 | Standing | 1.446309441915e12 | -3.17999 | -9.08132 | -3.8919e-2 |
| user_002 | Standing | 1.446309441923e12 | -3.14167 | -9.19628 | -0.15388 |
| user_002 | Standing | 1.446309441931e12 | -3.21831 | -9.08132 | -0.1922 |
| user_002 | Standing | 1.446309441939e12 | -3.06503 | -9.00468 | -0.268841 |
| user_002 | Standing | 1.446309441947e12 | -3.29495 | -8.92803 | -0.11556 |
| user_002 | Standing | 1.446309441956e12 | -3.21831 | -9.00468 | -0.307161 |
| user_002 | Standing | 1.446309441964e12 | -3.06503 | -8.88971 | -0.268841 |
| user_002 | Standing | 1.446309441971e12 | -3.25663 | -9.00468 | -0.15388 |
| user_002 | Standing | 1.446309441979e12 | -3.25663 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309441987e12 | -3.29495 | -8.92803 | -0.1922 |
| user_002 | Standing | 1.446309441995e12 | -3.33327 | -8.96635 | -0.268841 |
| user_002 | Standing | 1.446309442004e12 | -3.21831 | -8.88971 | -0.11556 |
| user_002 | Standing | 1.446309442012e12 | -3.25663 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.44630944202e12 | -3.06503 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442028e12 | -3.06503 | -9.11964 | -3.8919e-2 |
| user_002 | Standing | 1.446309442036e12 | -3.02671 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309442045e12 | -2.95007 | -9.19628 | -0.15388 |
| user_002 | Standing | 1.446309442052e12 | -2.91174 | -9.04299 | -0.1922 |
| user_002 | Standing | 1.446309442061e12 | -2.91174 | -9.19628 | -5.99e-4 |
| user_002 | Standing | 1.446309442068e12 | -2.95007 | -9.08132 | -5.99e-4 |
| user_002 | Standing | 1.446309442077e12 | -3.10335 | -9.08132 | -7.7239e-2 |
| user_002 | Standing | 1.446309442085e12 | -2.98839 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442092e12 | -3.10335 | -9.15796 | -0.268841 |
| user_002 | Standing | 1.446309442157e12 | -3.02671 | -8.92803 | -7.7239e-2 |
| user_002 | Standing | 1.446309442223e12 | -3.14167 | -8.92803 | -3.8919e-2 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309442352e12 | -2.72014 | -9.11964 | -7.7239e-2 |
| user_002 | Standing | 1.446309442417e12 | -2.56686 | -9.19628 | -3.8919e-2 |
| user_002 | Standing | 1.446309442481e12 | -2.6435 | -9.11964 | 3.7722e-2 |
| user_002 | Standing | 1.446309442547e12 | -2.56686 | -9.11964 | -7.7239e-2 |
| user_002 | Standing | 1.446309442611e12 | -2.60518 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.446309442675e12 | -2.4519 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.44630944274e12 | -2.41358 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.446309442805e12 | -2.41358 | -9.08132 | -7.7239e-2 |
| user_002 | Standing | 1.44630944287e12 | -2.33694 | -9.15796 | -7.7239e-2 |
| user_002 | Standing | 1.446309442935e12 | -2.37526 | -9.15796 | -5.99e-4 |
| user_002 | Standing | 1.446309443e12 | -2.29862 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309443064e12 | -2.2603 | -9.2346 | -0.11556 |
| user_002 | Standing | 1.446309443129e12 | -2.22198 | -9.2346 | -0.11556 |
| user_002 | Standing | 1.446309443194e12 | -2.10702 | -9.27292 | -3.8919e-2 |
| user_002 | Standing | 1.446309443258e12 | -2.18366 | -9.15796 | 3.7722e-2 |
| user_002 | Standing | 1.446309443323e12 | -2.2603 | -9.19628 | -0.11556 |
| user_002 | Standing | 1.446309443388e12 | -2.2603 | -9.15796 | -5.99e-4 |
| user_002 | Standing | 1.446309443453e12 | -2.10702 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.446309443517e12 | -2.03038 | -9.31124 | -3.8919e-2 |
| user_002 | Standing | 1.446309443582e12 | -2.14534 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309443647e12 | -2.29862 | -9.19628 | -5.99e-4 |
| user_002 | Standing | 1.446309443711e12 | -2.29862 | -9.19628 | 3.7722e-2 |
| user_002 | Standing | 1.446309443776e12 | -2.18366 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309443841e12 | -2.10702 | -9.19628 | 3.7722e-2 |
| user_002 | Standing | 1.446309443906e12 | -2.10702 | -9.15796 | 3.7722e-2 |
| user_002 | Standing | 1.446309443971e12 | -2.29862 | -9.15796 | 0.114362 |
| user_002 | Standing | 1.446309444036e12 | -2.03038 | -9.27292 | -5.99e-4 |
| user_002 | Standing | 1.4463094441e12 | -2.10702 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309444165e12 | -2.14534 | -9.2346 | 3.7722e-2 |
| user_002 | Standing | 1.44630944423e12 | -2.22198 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309444295e12 | -2.33694 | -9.19628 | 0.191003 |
| user_002 | Standing | 1.446309444359e12 | -2.10702 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309444424e12 | -2.0687 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.446309444489e12 | -2.03038 | -9.2346 | 0.114362 |
| user_002 | Standing | 1.446309444554e12 | -2.41358 | -9.00468 | 0.191003 |
| user_002 | Standing | 1.446309444618e12 | -2.37526 | -9.19628 | 0.267643 |
| user_002 | Standing | 1.446309444683e12 | -2.14534 | -9.2346 | 7.6042e-2 |
| user_002 | Standing | 1.446309444748e12 | -2.0687 | -9.27292 | 7.6042e-2 |
| user_002 | Standing | 1.446309444812e12 | -2.33694 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309444877e12 | -2.2603 | -9.19628 | 0.229323 |
| user_002 | Standing | 1.446309444941e12 | -2.18366 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309445007e12 | -2.22198 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309445072e12 | -2.22198 | -9.15796 | 0.191003 |
| user_002 | Standing | 1.446309445137e12 | -2.10702 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309445202e12 | -2.10702 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309445265e12 | -1.91542 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309445331e12 | -1.83878 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309445395e12 | -1.91542 | -9.08132 | 0.305964 |
| user_002 | Standing | 1.446309445459e12 | -1.95374 | -9.19628 | 0.152682 |
| user_002 | Standing | 1.446309445525e12 | -1.83878 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.44630944559e12 | -1.80046 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309445654e12 | -1.68549 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446309445719e12 | -1.80046 | -9.19628 | 0.267643 |
| user_002 | Standing | 1.446309445784e12 | -1.76214 | -9.34956 | -5.99e-4 |
| user_002 | Standing | 1.446309445849e12 | -1.91542 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309445913e12 | -1.80046 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309445978e12 | -1.76214 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309446043e12 | -1.83878 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309446108e12 | -1.83878 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309446173e12 | -1.8771 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309446237e12 | -1.8771 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309446302e12 | -1.8771 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309446367e12 | -1.8771 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309446432e12 | -1.8771 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309446496e12 | -1.91542 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309446561e12 | -1.8771 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309446625e12 | -1.83878 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309446691e12 | -1.91542 | -9.27292 | 7.6042e-2 |
| user_002 | Standing | 1.446309446755e12 | -1.8771 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.44630944682e12 | -1.80046 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309446884e12 | -1.80046 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.44630944695e12 | -1.72382 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447014e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447079e12 | -1.83878 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309447144e12 | -1.8771 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309447209e12 | -1.76214 | -9.19628 | 0.152682 |
| user_002 | Standing | 1.446309447273e12 | -1.72382 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309447338e12 | -1.83878 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309447403e12 | -1.8771 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309447467e12 | -1.83878 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447532e12 | -1.68549 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.446309447597e12 | -1.68549 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309447662e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309447726e12 | -1.8771 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309447791e12 | -1.80046 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309447855e12 | -1.64717 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309447921e12 | -1.60885 | -9.27292 | 0.114362 |
| user_002 | Standing | 1.446309447986e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.44630944805e12 | -1.76214 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309448115e12 | -1.72382 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.44630944818e12 | -1.72382 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309448245e12 | -1.64717 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309448309e12 | -1.64717 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309448374e12 | -1.72382 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309448439e12 | -1.72382 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.446309448504e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309448569e12 | -1.64717 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309448633e12 | -1.72382 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309448698e12 | -1.68549 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309448763e12 | -1.68549 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309448827e12 | -1.64717 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309448892e12 | -1.64717 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446309448957e12 | -1.60885 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449022e12 | -1.60885 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309449087e12 | -1.68549 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449151e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449216e12 | -1.68549 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309449281e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449346e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.44630944941e12 | -1.64717 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.446309449475e12 | -1.76214 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.44630944954e12 | -1.64717 | -9.31124 | -5.99e-4 |
| user_002 | Standing | 1.446309449605e12 | -1.64717 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449669e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449734e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449799e12 | -1.76214 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309449864e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449928e12 | -1.72382 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449993e12 | -1.72382 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450057e12 | -1.72382 | -9.34956 | 0.114362 |
| user_002 | Standing | 1.446309450123e12 | -1.72382 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309450188e12 | -1.68549 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450252e12 | -1.83878 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309450317e12 | -1.68549 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309450381e12 | -1.72382 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309450446e12 | -1.68549 | -9.34956 | 3.7722e-2 |
| user_002 | Standing | 1.446309450511e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309450576e12 | -1.76214 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309450641e12 | -1.72382 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309450705e12 | -1.64717 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.44630945077e12 | -1.72382 | -9.34956 | 0.114362 |
| user_002 | Standing | 1.446309450835e12 | -1.83878 | -9.2346 | 0.191003 |
dataDF.count()
res5: Long = 13679
dataDF.select($"user_id").distinct().show()
+--------+
| user_id|
+--------+
|user_002|
|user_006|
|user_005|
|user_001|
|user_007|
|user_003|
|user_004|
+--------+
dataDF.select($"activity").distinct().show()
+--------+
|activity|
+--------+
| Sitting|
| Walking|
| Jumping|
|Standing|
| Jogging|
+--------+
val sDF = dataDF.sample(false,0.105, 12345L)
sDF.count
sDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 4 more fields]
res8: Long = 1427
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong| x| y| z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping| 1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping| 1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping| 1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping| 1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping| 1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong| x| y| z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping| 1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping| 1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping| 1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping| 1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping| 1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
display(dataDF.sample(false,0.1))
| user_id | activity | timeStampAsLong | x | y | z |
|---|---|---|---|---|---|
| user_001 | Jumping | 1.446047227799e12 | 0.690364 | -3.7722e-2 | 1.72382 |
| user_001 | Jumping | 1.44604722793e12 | 1.87829 | -1.91542 | 0.880768 |
| user_001 | Jumping | 1.446047227995e12 | 1.57173 | -5.86241 | -3.75599 |
| user_001 | Jumping | 1.446047228383e12 | 0.230521 | 2.0699 | -1.41845 |
| user_001 | Jumping | 1.446047228512e12 | 1.53341 | -0.305964 | 1.41725 |
| user_001 | Jumping | 1.446047228707e12 | 7.43474 | -19.58108 | 2.95007 |
| user_001 | Jumping | 1.446047228966e12 | -1.30229 | 2.2615 | -1.26517 |
| user_001 | Jumping | 1.446047230195e12 | -2.41358 | 1.91661 | -5.99e-4 |
| user_001 | Jumping | 1.446047230584e12 | 8.00954 | -19.58108 | -0.1922 |
| user_001 | Jumping | 1.446047231296e12 | 11.61165 | -19.58108 | 4.25296 |
| user_001 | Jumping | 1.446047233562e12 | 0.996927 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.44604723421e12 | 2.79798 | -0.114362 | 0.919089 |
| user_001 | Jumping | 1.446047234468e12 | 6.89825 | -19.58108 | -4.25415 |
| user_001 | Jumping | 1.446047234663e12 | 0.805325 | 1.15021 | -0.15388 |
| user_001 | Jumping | 1.446047234986e12 | 5.36544 | -4.09967 | -0.537083 |
| user_001 | Jumping | 1.446047235115e12 | 6.93658 | -19.58108 | -2.75966 |
| user_001 | Jumping | 1.446047235633e12 | 1.18853 | -2.72014 | 1.41725 |
| user_001 | Jumping | 1.446047235763e12 | 6.55337 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047236281e12 | 3.56439 | -5.90073 | 0.727487 |
| user_001 | Jumping | 1.446047236346e12 | 7.05154 | -17.97163 | -1.61005 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 |
| user_001 | Jumping | 1.446047236798e12 | 1.80165 | 0.345482 | 0.765807 |
| user_001 | Jumping | 1.446047236928e12 | 3.48775 | -7.31858 | -0.728685 |
| user_001 | Jumping | 1.446047236992e12 | 5.32712 | -18.92964 | 0.114362 |
| user_001 | Jumping | 1.446047237057e12 | 10.34708 | -19.58108 | 2.37526 |
| user_001 | Jumping | 1.44604723764e12 | 4.98224 | -19.27452 | 1.57053 |
| user_001 | Jumping | 1.446047238287e12 | 6.32345 | -17.62674 | -0.728685 |
| user_001 | Jumping | 1.446047238676e12 | 1.95493 | 0.230521 | 1.60885 |
| user_001 | Jumping | 1.446047241849e12 | -0.919089 | 2.6447 | -3.14286 |
| user_001 | Jumping | 1.446047243725e12 | -1.07237 | 1.15021 | 0.152682 |
| user_001 | Jumping | 1.446047243984e12 | 2.87462 | -0.535886 | -0.268841 |
| user_001 | Jumping | 1.446047244243e12 | 14.86888 | -19.58108 | -1.41845 |
| user_003 | Jumping | 1.446047778293e12 | -3.63983 | -9.04299 | -2.6447 |
| user_003 | Jumping | 1.446047780171e12 | -3.37159 | -5.78577 | -3.87095 |
| user_003 | Jumping | 1.446047780365e12 | -3.86975 | -19.58108 | -5.40376 |
| user_003 | Jumping | 1.446047781725e12 | 0.230521 | 0.268841 | -0.345482 |
| user_003 | Jumping | 1.446047782244e12 | -1.45557 | -7.85507 | -1.22685 |
| user_003 | Jumping | 1.446047782373e12 | -1.64717 | -5.90073 | -2.33814 |
| user_003 | Jumping | 1.446047783151e12 | -1.14901 | -14.98264 | -4.13919 |
| user_003 | Jumping | 1.446047783409e12 | -3.67815 | -4.44456 | -2.4531 |
| user_003 | Jumping | 1.446047784057e12 | -2.8351 | -17.62674 | -5.02056 |
| user_003 | Jumping | 1.44604778451e12 | -0.344284 | -6.51385 | -2.8363 |
| user_003 | Jumping | 1.446047784575e12 | -4.02303 | -14.98264 | -2.2615 |
| user_003 | Jumping | 1.446047784769e12 | -2.4519 | 7.7239e-2 | 1.11069 |
| user_003 | Jumping | 1.446047785158e12 | -4.3296 | -19.38948 | -5.17384 |
| user_003 | Jumping | 1.446047785287e12 | -3.44823 | -7.24194 | -2.18486 |
| user_003 | Jumping | 1.44604778587e12 | -0.919089 | -4.82776 | -0.268841 |
| user_003 | Jumping | 1.446047786129e12 | -0.842448 | -0.497565 | -3.0279 |
| user_003 | Jumping | 1.446047786453e12 | -3.94639 | -5.59417 | -3.14286 |
| user_003 | Jumping | 1.44604778723e12 | -0.880768 | 0.422122 | -1.03525 |
| user_003 | Jumping | 1.446047787358e12 | -4.59784 | -19.35116 | -5.94025 |
| user_003 | Jumping | 1.446047787553e12 | -3.44823 | -6.47553 | -2.75966 |
| user_003 | Jumping | 1.446047788136e12 | -3.14167 | 2.72134 | -0.575403 |
| user_003 | Jumping | 1.446047788719e12 | -2.10702 | -7.31858 | -2.10822 |
| user_003 | Jumping | 1.446047789301e12 | 0.843646 | -0.382604 | 1.11069 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 |
| user_003 | Jumping | 1.446047789818e12 | -2.03038 | -6.85874 | -1.68669 |
| user_003 | Jumping | 1.446047790078e12 | -5.51753 | -19.58108 | -7.24314 |
| user_003 | Jumping | 1.446047790726e12 | -1.26397 | -8.12331 | -3.10454 |
| user_003 | Jumping | 1.446047790919e12 | -1.83878 | -3.40991 | -3.60271 |
| user_003 | Jumping | 1.446047791114e12 | -7.70178 | -19.58108 | -7.5497 |
| user_003 | Jumping | 1.446047791762e12 | -2.14534 | -9.84772 | -3.2195 |
| user_003 | Jumping | 1.446047792021e12 | -2.95007 | -5.096 | -2.52974 |
| user_003 | Jumping | 1.446047792669e12 | -2.87342 | -18.96796 | -5.86361 |
| user_003 | Jumping | 1.446047793834e12 | -3.52487 | -13.87135 | -5.63368 |
| user_003 | Jumping | 1.446047794158e12 | -1.57053 | -5.90073 | -3.2195 |
| user_002 | Standing | 1.446309439342e12 | -2.49022 | -9.19628 | -1.11189 |
| user_002 | Standing | 1.446309439373e12 | -2.75846 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309439584e12 | -2.52854 | -8.88971 | -1.15021 |
| user_002 | Standing | 1.446309439665e12 | -2.8351 | -9.2346 | -0.920286 |
| user_002 | Standing | 1.446309439924e12 | -3.10335 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309440021e12 | -2.91174 | -9.08132 | -1.22685 |
| user_002 | Standing | 1.446309440045e12 | -3.06503 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440111e12 | -3.10335 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440167e12 | -3.33327 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440239e12 | -3.14167 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309440426e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440498e12 | -3.33327 | -8.96635 | -0.805325 |
| user_002 | Standing | 1.446309440604e12 | -2.87342 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309440887e12 | -3.17999 | -8.96635 | -0.345482 |
| user_002 | Standing | 1.446309440951e12 | -3.21831 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440976e12 | -3.17999 | -8.81307 | -0.728685 |
| user_002 | Standing | 1.446309441017e12 | -2.91174 | -9.08132 | -0.652044 |
| user_002 | Standing | 1.446309441049e12 | -2.87342 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309441276e12 | -3.29495 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441341e12 | -3.37159 | -9.08132 | -0.460442 |
| user_002 | Standing | 1.446309441349e12 | -3.44823 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441453e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441501e12 | -3.29495 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441591e12 | -3.40991 | -8.73643 | -0.230521 |
| user_002 | Standing | 1.446309441615e12 | -3.40991 | -8.77475 | -0.345482 |
| user_002 | Standing | 1.446309441664e12 | -3.17999 | -9.2346 | -0.460442 |
| user_002 | Standing | 1.446309441688e12 | -3.29495 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441793e12 | -3.17999 | -9.2346 | -0.307161 |
| user_002 | Standing | 1.446309441842e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.446309441882e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441907e12 | -3.17999 | -8.77475 | -0.230521 |
| user_002 | Standing | 1.446309441915e12 | -3.17999 | -9.08132 | -3.8919e-2 |
| user_002 | Standing | 1.446309441979e12 | -3.25663 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309442085e12 | -2.98839 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309442417e12 | -2.56686 | -9.19628 | -3.8919e-2 |
| user_002 | Standing | 1.446309443323e12 | -2.2603 | -9.19628 | -0.11556 |
| user_002 | Standing | 1.446309443776e12 | -2.18366 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309444036e12 | -2.03038 | -9.27292 | -5.99e-4 |
| user_002 | Standing | 1.446309444165e12 | -2.14534 | -9.2346 | 3.7722e-2 |
| user_002 | Standing | 1.446309444295e12 | -2.33694 | -9.19628 | 0.191003 |
| user_002 | Standing | 1.446309444424e12 | -2.0687 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.44630944805e12 | -1.76214 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309448763e12 | -1.68549 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309449151e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449346e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309449993e12 | -1.72382 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450317e12 | -1.68549 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309450576e12 | -1.76214 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309450835e12 | -1.83878 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309452195e12 | -1.76214 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309452389e12 | -1.72382 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309452907e12 | -1.72382 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309453685e12 | -1.76214 | -9.19628 | 0.191003 |
| user_002 | Standing | 1.446309453749e12 | -1.68549 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309454915e12 | -1.76214 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309455109e12 | -1.80046 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309455239e12 | -1.72382 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309455562e12 | -1.68549 | -9.27292 | 0.114362 |
| user_005 | Standing | 1.446046599619e12 | -7.6042e-2 | -9.50284 | -0.498763 |
| user_005 | Standing | 1.446046601883e12 | 0.307161 | -9.4262 | 0.650847 |
| user_005 | Standing | 1.446046602014e12 | 0.307161 | -9.4262 | 0.612526 |
| user_005 | Standing | 1.446046602661e12 | 0.307161 | -9.34956 | 0.497565 |
| user_005 | Standing | 1.446046602855e12 | 0.345482 | -9.46452 | 0.459245 |
| user_005 | Standing | 1.446046603114e12 | 0.345482 | -9.38788 | 0.420925 |
| user_005 | Standing | 1.446046603373e12 | 0.460442 | -9.4262 | 0.497565 |
| user_005 | Standing | 1.44604660402e12 | 0.307161 | -9.34956 | 0.459245 |
| user_005 | Standing | 1.446046604473e12 | 0.307161 | -9.38788 | 0.382604 |
| user_005 | Standing | 1.446046605315e12 | 0.307161 | -9.38788 | 0.459245 |
| user_005 | Standing | 1.446046606867e12 | 0.268841 | -9.38788 | 0.459245 |
| user_005 | Standing | 1.446046607968e12 | 0.307161 | -9.46452 | 0.382604 |
| user_005 | Standing | 1.446046608033e12 | 0.307161 | -9.46452 | 0.420925 |
| user_005 | Standing | 1.446046608356e12 | 0.307161 | -9.4262 | 0.420925 |
| user_005 | Standing | 1.446046608745e12 | 0.345482 | -9.38788 | 0.420925 |
| user_005 | Standing | 1.446046609263e12 | 0.383802 | -9.38788 | 0.420925 |
| user_005 | Standing | 1.446046609586e12 | 0.307161 | -9.46452 | 0.459245 |
| user_005 | Standing | 1.446046610751e12 | 0.307161 | -9.4262 | 0.382604 |
| user_005 | Standing | 1.446046611463e12 | 0.307161 | -9.4262 | 0.344284 |
| user_005 | Standing | 1.44604661224e12 | 0.307161 | -9.4262 | 0.459245 |
| user_005 | Standing | 1.446046612823e12 | 0.345482 | -9.46452 | 0.420925 |
| user_005 | Standing | 1.446046614182e12 | 0.307161 | -9.4262 | 0.191003 |
| user_005 | Standing | 1.446046614895e12 | 0.15388 | -9.19628 | 0.382604 |
| user_005 | Standing | 1.446046615089e12 | 0.230521 | -9.4262 | 0.229323 |
| user_005 | Standing | 1.446046615736e12 | 0.383802 | -9.4262 | 0.191003 |
| user_002 | Sitting | 1.446034564845e12 | -0.842448 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034566335e12 | -0.650847 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034566464e12 | -0.727487 | -0.114362 | 9.38788 |
| user_002 | Sitting | 1.446034566528e12 | -0.727487 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034566918e12 | -0.804128 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034567047e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034567695e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034568082e12 | -0.689167 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034568601e12 | -0.765807 | -0.191003 | 9.34956 |
| user_002 | Sitting | 1.44603456873e12 | -0.727487 | -0.114362 | 9.50284 |
| user_002 | Sitting | 1.446034569701e12 | -0.804128 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034570089e12 | -0.765807 | -7.6042e-2 | 9.54116 |
| user_002 | Sitting | 1.446034570154e12 | -0.727487 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034571191e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034571902e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034572873e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034574038e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034574103e12 | -0.727487 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034574492e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034574751e12 | -0.727487 | -0.229323 | 9.4262 |
| user_002 | Sitting | 1.446034574816e12 | -0.727487 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034575916e12 | -0.765807 | -0.114362 | 9.38788 |
| user_002 | Sitting | 1.446034576759e12 | -0.727487 | -0.229323 | 9.4262 |
| user_002 | Sitting | 1.446034577018e12 | -0.727487 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034578313e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034578377e12 | -0.727487 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034578571e12 | -0.765807 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034578766e12 | -0.765807 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034580125e12 | -0.727487 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034581421e12 | -0.842448 | -0.152682 | 9.4262 |
| user_006 | Jogging | 1.446046855308e12 | -9.69444 | -10.49917 | -3.60271 |
| user_006 | Jogging | 1.446046856149e12 | -7.5485 | -9.19628 | -3.06622 |
| user_006 | Jogging | 1.446046857249e12 | -10.11596 | -10.15428 | -5.78697 |
| user_006 | Jogging | 1.446046857573e12 | -12.18526 | -10.76741 | -3.8919e-2 |
| user_006 | Jogging | 1.446046857962e12 | -8.12331 | -17.78002 | -3.25783 |
| user_006 | Jogging | 1.446046859581e12 | -4.55952 | -2.0687 | -2.60638 |
| user_006 | Jogging | 1.44604685971e12 | -6.51385 | -6.28393 | -7.58802 |
| user_006 | Jogging | 1.446046860164e12 | -9.08132 | -12.56846 | -6.24681 |
| user_006 | Jogging | 1.446046860293e12 | -5.70913 | -3.48655 | -4.06255 |
| user_006 | Jogging | 1.446046860423e12 | -2.8351 | -4.5212 | -4.13919 |
| user_006 | Jogging | 1.446046862495e12 | -4.36792 | -1.07237 | -1.72501 |
| user_006 | Jogging | 1.446046862948e12 | -3.37159 | -1.03405 | -1.26517 |
| user_006 | Jogging | 1.446046863789e12 | -6.09233 | -16.9753 | -2.03158 |
| user_006 | Jogging | 1.446046863854e12 | -7.39522 | -9.65612 | -5.63368 |
| user_006 | Jogging | 1.446046864372e12 | -3.75479 | -2.10702 | -2.68302 |
| user_006 | Jogging | 1.446046865149e12 | -2.41358 | -1.22565 | -0.15388 |
| user_006 | Jogging | 1.446046865472e12 | -2.18366 | 1.15021 | -1.95493 |
| user_006 | Jogging | 1.446046865861e12 | -2.68182 | -2.41358 | -2.33814 |
| user_006 | Jogging | 1.44604686612e12 | -6.82042 | -6.39889 | -3.79431 |
| user_006 | Jogging | 1.446046867867e12 | -12.37686 | -13.71807 | 7.6042e-2 |
| user_006 | Jogging | 1.446046868061e12 | -3.44823 | -6.28393 | -3.83263 |
| user_006 | Jogging | 1.446046868644e12 | -12.14694 | -10.30757 | 2.14534 |
| user_006 | Jogging | 1.446046868709e12 | -9.6178 | -10.38421 | -4.10087 |
| user_006 | Jogging | 1.446046870263e12 | -10.26924 | -8.65979 | -2.91294 |
| user_005 | Jogging | 1.446046533136e12 | -1.95374 | -10.34589 | 0.267643 |
| user_005 | Jogging | 1.446046533912e12 | 12.10982 | -19.58108 | -4.82896 |
| user_005 | Jogging | 1.446046534042e12 | 4.21583 | 11.30509 | 8.27659 |
| user_005 | Jogging | 1.446046535143e12 | -5.47921 | -0.765807 | -6.13185 |
| user_005 | Jogging | 1.446046536049e12 | 14.3324 | -19.58108 | -2.68302 |
| user_005 | Jogging | 1.446046536113e12 | -2.75846 | -16.24721 | -3.90927 |
| user_005 | Jogging | 1.446046536566e12 | 2.72134 | -6.85874 | -8.54603 |
| user_005 | Jogging | 1.446046537247e12 | 2.33814 | -11.8787 | -8.46939 |
| user_005 | Jogging | 1.446046537287e12 | -3.06503 | -0.574206 | -10.04052 |
| user_005 | Jogging | 1.446046537304e12 | -13.29655 | -1.26397 | -7.31978 |
| user_005 | Jogging | 1.446046537361e12 | -1.41725 | -7.6042e-2 | -7.58802 |
| user_005 | Jogging | 1.446046537385e12 | 0.537083 | -1.14901 | 0.957409 |
| user_005 | Jogging | 1.446046537555e12 | -2.87342 | -13.64143 | 1.64717 |
| user_005 | Jogging | 1.446046537927e12 | -3.83143 | -18.50811 | -14.17911 |
| user_005 | Jogging | 1.446046538154e12 | 3.75599 | -19.58108 | -1.11189 |
| user_005 | Jogging | 1.44604653821e12 | 11.30509 | -19.58108 | -7.85626 |
| user_005 | Jogging | 1.446046538218e12 | 7.31978 | -19.58108 | -10.04052 |
| user_005 | Jogging | 1.446046538267e12 | -2.60518 | -11.41886 | 1.83878 |
| user_005 | Jogging | 1.446046538275e12 | 0.11556 | -4.9044 | 1.64717 |
| user_005 | Jogging | 1.44604653838e12 | 0.996927 | 3.06622 | 2.2603 |
| user_005 | Jogging | 1.446046538388e12 | 7.7239e-2 | 1.45677 | 1.37893 |
| user_005 | Jogging | 1.446046538477e12 | 2.72134 | -10.80573 | 7.66346 |
| user_005 | Jogging | 1.446046538502e12 | 16.55497 | -17.01362 | 12.03198 |
| user_005 | Jogging | 1.446046538696e12 | 8.31611 | -6.74378 | -7.08986 |
| user_005 | Jogging | 1.446046538898e12 | 19.00747 | -19.58108 | -5.25048 |
| user_005 | Jogging | 1.446046538971e12 | -7.43354 | -18.58475 | 0.114362 |
| user_005 | Jogging | 1.446046539084e12 | 1.49509 | 2.95126 | 3.90807 |
| user_005 | Jogging | 1.446046539165e12 | 3.44943 | -14.3312 | -7.70298 |
| user_005 | Jogging | 1.446046539214e12 | 19.50564 | -15.0976 | -9.619 |
| user_005 | Jogging | 1.446046539229e12 | 7.89458 | -15.02096 | 10.92069 |
| user_005 | Jogging | 1.446046539238e12 | 2.98958 | -12.10862 | 10.92069 |
| user_005 | Jogging | 1.446046539521e12 | 5.99e-4 | -3.98471 | -0.690364 |
| user_005 | Jogging | 1.446046539659e12 | -4.86608 | -19.58108 | -6.13185 |
| user_005 | Jogging | 1.446046539708e12 | 2.8363 | 12.4547 | 5.44089 |
| user_005 | Jogging | 1.446046539958e12 | -0.880768 | -9.04299 | 11.6871 |
| user_005 | Jogging | 1.446046540023e12 | -7.66346 | -19.58108 | -3.60271 |
| user_005 | Jogging | 1.446046540031e12 | -4.21464 | -19.58108 | -10.27044 |
| user_005 | Jogging | 1.44604654008e12 | 11.42005 | -7.66346 | -4.67568 |
| user_005 | Jogging | 1.446046540153e12 | -12.98999 | -3.79311 | -1.91661 |
| user_005 | Jogging | 1.446046540169e12 | -6.51385 | -2.75846 | -10.50036 |
| user_005 | Jogging | 1.446046540201e12 | 3.8919e-2 | -3.37159 | -0.11556 |
| user_005 | Jogging | 1.446046540355e12 | -6.85874 | -19.58108 | -4.63736 |
| user_005 | Jogging | 1.446046540387e12 | -0.765807 | 8.50771 | 7.39522 |
| user_005 | Jogging | 1.446046540395e12 | 1.87829 | 12.60798 | 8.42987 |
| user_005 | Jogging | 1.446046540587e12 | 12.03318 | -8.85139 | 14.17792 |
| user_005 | Jogging | 1.446046540595e12 | 17.1681 | -10.84405 | 12.53014 |
| user_005 | Jogging | 1.446046540636e12 | -2.87342 | -9.77108 | 12.72175 |
| user_005 | Jogging | 1.446046540659e12 | 0.345482 | -9.84772 | 8.27659 |
| user_005 | Jogging | 1.446046540766e12 | 9.54236 | -8.81307 | -9.12083 |
| user_005 | Jogging | 1.446046540772e12 | 9.84892 | -3.83143 | -8.16283 |
| user_005 | Jogging | 1.446046540813e12 | -18.00995 | -3.79311 | -9.15915 |
| user_005 | Jogging | 1.446046540838e12 | -10.72909 | -3.25663 | -3.2195 |
| user_005 | Jogging | 1.446046540976e12 | 3.2195 | -19.58108 | -6.9749 |
| user_005 | Jogging | 1.446046541105e12 | 0.690364 | 15.78857 | 11.91702 |
| user_005 | Jogging | 1.446046541332e12 | -2.10702 | -6.82042 | 11.84038 |
| user_005 | Jogging | 1.446046541348e12 | -1.22565 | -12.4535 | 10.69077 |
| user_005 | Jogging | 1.446046541445e12 | 15.40536 | -4.55952 | -2.8363 |
| user_005 | Jogging | 1.446046541477e12 | -9.92436 | -2.8351 | -14.29407 |
| user_005 | Jogging | 1.44604654155e12 | -0.497565 | -1.80046 | -0.920286 |
| user_005 | Jogging | 1.446046541638e12 | 6.43841 | -19.58108 | -5.78697 |
| user_005 | Jogging | 1.446046541841e12 | 1.83997 | 1.61005 | 0.612526 |
| user_005 | Jogging | 1.446046541906e12 | 8.69931 | -19.58108 | -17.62794 |
| user_005 | Jogging | 1.446046542011e12 | -1.95374 | -5.74745 | 11.57214 |
| user_005 | Jogging | 1.446046542116e12 | 10.0022 | -9.04299 | -7.28146 |
| user_005 | Jogging | 1.446046542189e12 | -14.1396 | -4.7128 | -4.82896 |
| user_005 | Jogging | 1.446046542262e12 | -1.45557 | -4.02303 | -2.56806 |
| user_005 | Jogging | 1.446046542295e12 | -5.67081 | -10.001 | 5.01936 |
| user_005 | Jogging | 1.446046542441e12 | -0.995729 | -3.17999 | 10.26924 |
| user_005 | Jogging | 1.446046542651e12 | 1.53341 | -16.82202 | 19.08292 |
| user_005 | Jogging | 1.446046542659e12 | 0.690364 | -9.2346 | 11.34221 |
| user_005 | Jogging | 1.446046542699e12 | -0.382604 | -7.24194 | 6.59049 |
| user_005 | Jogging | 1.446046542715e12 | 1.91661 | -16.70706 | 2.72014 |
| user_005 | Jogging | 1.446046542739e12 | 1.07357 | -19.58108 | -14.63896 |
| user_005 | Jogging | 1.446046542812e12 | -0.420925 | -7.31858 | -8.85259 |
| user_005 | Jogging | 1.44604654282e12 | 0.805325 | -5.74745 | -9.15915 |
| user_005 | Jogging | 1.446046542853e12 | -3.21831 | 1.95493 | -1.26517 |
| user_005 | Jogging | 1.446046543063e12 | 6.01689 | -19.54276 | -6.05521 |
| user_005 | Jogging | 1.446046543071e12 | 2.60638 | -17.93331 | -3.0279 |
| user_005 | Jogging | 1.446046543136e12 | 0.805325 | -0.305964 | 7.66346 |
| user_005 | Jogging | 1.446046543209e12 | 2.72134 | 9.12083 | 1.30229 |
| user_005 | Jogging | 1.446046543233e12 | 4.48408 | 1.22685 | -3.37279 |
| user_005 | Jogging | 1.44604654333e12 | 3.44943 | -19.58108 | 16.4005 |
| user_005 | Jogging | 1.446046543379e12 | -1.57053 | -19.58108 | 2.29862 |
| user_005 | Jogging | 1.446046543444e12 | 8.69931 | -5.59417 | -9.84892 |
| user_005 | Jogging | 1.446046543573e12 | -2.10702 | -1.22565 | -0.613724 |
| user_005 | Jogging | 1.446046543621e12 | 0.11556 | -8.12331 | 4.25296 |
| user_005 | Jogging | 1.446046543826e12 | 11.49669 | -3.02671 | -6.66833 |
| user_005 | Jogging | 1.446046543859e12 | 8.31611 | 1.41845 | -10.0022 |
| user_005 | Jogging | 1.446046544045e12 | -1.14901 | -16.20889 | -5.74865 |
| user_005 | Jogging | 1.446046544118e12 | -1.37893 | -15.32753 | -6.9749 |
| user_005 | Jogging | 1.446046544126e12 | -0.612526 | -14.75272 | -7.39642 |
| user_005 | Jogging | 1.446046544295e12 | 1.07357 | -7.3569 | -1.53341 |
| user_005 | Jogging | 1.44604654432e12 | -0.689167 | -13.29655 | -1.30349 |
| user_005 | Jogging | 1.446046544352e12 | -5.70913 | -19.58108 | -6.09353 |
| user_005 | Jogging | 1.446046544465e12 | 2.6447 | 0.422122 | 3.52487 |
| user_005 | Jogging | 1.446046544498e12 | 4.86728 | 2.91294 | 3.63983 |
| user_005 | Jogging | 1.446046544554e12 | 2.72134 | 0.268841 | 0.765807 |
| user_005 | Jogging | 1.44604654466e12 | -2.10702 | -9.54116 | 10.11596 |
| user_005 | Jogging | 1.446046544668e12 | 1.18853 | -14.86768 | 12.14694 |
| user_005 | Jogging | 1.446046544684e12 | 4.56072 | -19.58108 | 2.6435 |
| user_005 | Jogging | 1.446046544692e12 | 4.86728 | -19.58108 | 1.8771 |
| user_005 | Jogging | 1.446046544757e12 | -6.13065 | -19.58108 | 4.9044 |
| user_005 | Jogging | 1.446046544838e12 | 1.64837 | -12.53014 | -11.80326 |
| user_005 | Jogging | 1.446046544894e12 | -4.78944 | -2.0687 | -8.85259 |
| user_005 | Jogging | 1.446046545121e12 | -2.18366 | -11.15061 | -3.8919e-2 |
| user_005 | Jogging | 1.446046545202e12 | 3.0279 | 5.40376 | 4.5212 |
| user_005 | Jogging | 1.446046545267e12 | -0.344284 | 0.920286 | -1.11189 |
| user_005 | Jogging | 1.44604654538e12 | 2.91294 | -12.03198 | 4.75112 |
| user_005 | Jogging | 1.446046545485e12 | 1.61005 | -18.66139 | -12.2631 |
| user_005 | Jogging | 1.446046545639e12 | 3.8919e-2 | -4.02303 | -6.66833 |
| user_005 | Jogging | 1.446046545704e12 | 2.03158 | -19.12124 | 2.10702 |
| user_005 | Jogging | 1.446046545833e12 | -1.18733 | -4.55952 | 4.09967 |
| user_005 | Jogging | 1.446046545841e12 | 1.07357 | 1.03525 | 4.138 |
| user_005 | Jogging | 1.446046545866e12 | 5.36544 | 15.17544 | 7.39522 |
| user_005 | Jogging | 1.446046546052e12 | 7.43474 | -7.70178 | 6.82042 |
| user_005 | Jogging | 1.446046546068e12 | -0.689167 | -9.46452 | 11.07397 |
| user_005 | Jogging | 1.446046546229e12 | 6.32345 | -4.78944 | -6.13185 |
| user_005 | Jogging | 1.446046546368e12 | 0.881966 | -6.32225 | 1.49389 |
| user_005 | Jogging | 1.446046546392e12 | -0.574206 | -13.44983 | 3.75479 |
| user_005 | Jogging | 1.4460465464e12 | 1.11189 | -17.74171 | 1.83878 |
| user_005 | Jogging | 1.446046546424e12 | 14.29407 | -19.58108 | -5.74865 |
| user_005 | Jogging | 1.446046546457e12 | 16.05681 | -19.58108 | -2.60638 |
| user_005 | Jogging | 1.446046546554e12 | 3.79431 | 7.7413 | 8.12331 |
| user_005 | Jogging | 1.446046546683e12 | -2.79678 | -15.94065 | -19.54396 |
| user_005 | Jogging | 1.44604654682e12 | -0.191003 | -19.50444 | 6.09233 |
| user_005 | Jogging | 1.446046546861e12 | -3.44823 | -19.58108 | -6.70665 |
| user_005 | Jogging | 1.446046546877e12 | 1.22685 | -19.58108 | -15.2904 |
| user_005 | Jogging | 1.446046546893e12 | 1.03525 | -15.74905 | -9.54236 |
| user_005 | Jogging | 1.44604654695e12 | 1.49509 | 1.64837 | -8.62267 |
| user_005 | Jogging | 1.446046547015e12 | -5.93905 | 0.728685 | -11.68829 |
| user_005 | Jogging | 1.446046548148e12 | 7.5497 | -12.30022 | 3.67815 |
| user_005 | Jogging | 1.446046549119e12 | -7.66346 | -0.229323 | -6.59169 |
| user_005 | Jogging | 1.446046549378e12 | -2.52854 | -6.9737 | 2.10702 |
| user_005 | Jogging | 1.446046549572e12 | 8.35443 | -16.13225 | 1.64717 |
| user_005 | Jogging | 1.446046549702e12 | -2.49022 | -19.35116 | 1.30229 |
| user_006 | Standing | 1.446046930244e12 | -4.06135 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046930349e12 | -4.25296 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046930381e12 | -4.09967 | -8.50651 | -0.958607 |
| user_006 | Standing | 1.446046930421e12 | -4.29128 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046930591e12 | -4.17632 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046930633e12 | -3.98471 | -8.62147 | -1.18853 |
| user_006 | Standing | 1.446046930649e12 | -4.17632 | -8.46819 | -0.920286 |
| user_006 | Standing | 1.446046930746e12 | -4.3296 | -8.58315 | -0.920286 |
| user_006 | Standing | 1.446046930883e12 | -4.17632 | -8.42987 | -1.07357 |
| user_006 | Standing | 1.446046931005e12 | -4.25296 | -8.65979 | -1.11189 |
| user_006 | Standing | 1.446046931045e12 | -4.02303 | -8.65979 | -0.920286 |
| user_006 | Standing | 1.446046931053e12 | -4.09967 | -8.65979 | -0.920286 |
| user_006 | Standing | 1.446046931078e12 | -3.98471 | -8.58315 | -1.07357 |
| user_006 | Standing | 1.446046931401e12 | -4.25296 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046931482e12 | -4.36792 | -8.54483 | -1.11189 |
| user_006 | Standing | 1.446046931499e12 | -4.25296 | -8.65979 | -0.881966 |
| user_006 | Standing | 1.446046931563e12 | -4.138 | -8.58315 | -0.843646 |
| user_006 | Standing | 1.446046931595e12 | -4.36792 | -8.58315 | -1.03525 |
| user_006 | Standing | 1.446046931628e12 | -4.06135 | -8.50651 | -0.881966 |
| user_006 | Standing | 1.446046931733e12 | -4.17632 | -8.58315 | -0.958607 |
| user_006 | Standing | 1.446046931855e12 | -4.25296 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046931927e12 | -4.17632 | -8.62147 | -0.767005 |
| user_006 | Standing | 1.446046932178e12 | -4.138 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046932186e12 | -4.25296 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046932235e12 | -4.138 | -8.46819 | -0.920286 |
| user_006 | Standing | 1.446046932242e12 | -4.09967 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046932364e12 | -4.09967 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046932478e12 | -4.17632 | -8.65979 | -0.881966 |
| user_006 | Standing | 1.446046932575e12 | -4.29128 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.44604693264e12 | -4.17632 | -8.77475 | -0.843646 |
| user_006 | Standing | 1.446046932818e12 | -4.25296 | -8.58315 | -0.881966 |
| user_006 | Standing | 1.446046932826e12 | -4.25296 | -8.54483 | -0.996927 |
| user_006 | Standing | 1.446046932988e12 | -4.09967 | -8.50651 | -1.07357 |
| user_006 | Standing | 1.446046933012e12 | -4.138 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.44604693302e12 | -4.09967 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046933109e12 | -4.3296 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046933133e12 | -4.29128 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046933287e12 | -4.25296 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.446046933303e12 | -4.25296 | -8.58315 | -0.843646 |
| user_006 | Standing | 1.446046935092e12 | -4.138 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.446046935156e12 | -4.21464 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046935286e12 | -4.21464 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046936452e12 | -4.17632 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046936581e12 | -4.17632 | -8.65979 | -0.881966 |
| user_006 | Standing | 1.446046936776e12 | -4.17632 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046937359e12 | -4.17632 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046937941e12 | -4.21464 | -8.65979 | -0.958607 |
| user_006 | Standing | 1.446046938459e12 | -4.29128 | -8.62147 | -1.07357 |
| user_006 | Standing | 1.446046938586e12 | -4.5212 | -8.77475 | -1.07357 |
| user_006 | Standing | 1.446046938708e12 | -4.25296 | -8.54483 | -0.996927 |
| user_006 | Standing | 1.446046938829e12 | -4.21464 | -8.58315 | -1.26517 |
| user_006 | Standing | 1.446046938861e12 | -4.17632 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.446046938886e12 | -4.29128 | -8.58315 | -1.03525 |
| user_006 | Standing | 1.446046938893e12 | -4.17632 | -8.85139 | -0.881966 |
| user_006 | Standing | 1.446046939355e12 | -4.3296 | -8.69811 | -1.15021 |
| user_006 | Standing | 1.446046939363e12 | -4.25296 | -8.46819 | -0.881966 |
| user_006 | Standing | 1.446046939395e12 | -4.21464 | -8.65979 | -0.958607 |
| user_006 | Standing | 1.446046939412e12 | -4.29128 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.446046939428e12 | -4.17632 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046939468e12 | -4.138 | -8.62147 | -0.920286 |
| user_006 | Standing | 1.446046939533e12 | -4.21464 | -8.58315 | -1.11189 |
| user_006 | Standing | 1.446046939557e12 | -4.36792 | -8.54483 | -0.805325 |
| user_006 | Standing | 1.446046939679e12 | -4.138 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046939719e12 | -4.21464 | -8.65979 | -1.18853 |
| user_006 | Standing | 1.446046939897e12 | -4.21464 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.446046940018e12 | -4.21464 | -8.65979 | -1.11189 |
| user_006 | Standing | 1.446046940051e12 | -4.138 | -8.77475 | -0.996927 |
| user_006 | Standing | 1.446046940075e12 | -4.17632 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046940172e12 | -4.138 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.446046940205e12 | -4.21464 | -8.69811 | -1.07357 |
| user_006 | Standing | 1.446046940221e12 | -4.09967 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046940277e12 | -4.17632 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046940447e12 | -4.138 | -8.69811 | -1.07357 |
| user_006 | Standing | 1.446046940471e12 | -4.25296 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046940479e12 | -4.21464 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046940585e12 | -4.3296 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.44604694073e12 | -4.21464 | -8.50651 | -0.958607 |
| user_006 | Standing | 1.446046940851e12 | -4.21464 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046940868e12 | -4.09967 | -8.77475 | -1.07357 |
| user_006 | Standing | 1.446046940932e12 | -4.21464 | -8.73643 | -0.958607 |
| user_006 | Standing | 1.446046940981e12 | -4.17632 | -8.65979 | -1.15021 |
| user_006 | Standing | 1.446046941005e12 | -4.40624 | -8.77475 | -1.07357 |
| user_006 | Standing | 1.446046941102e12 | -4.40624 | -8.54483 | -1.22685 |
| user_006 | Standing | 1.446046941135e12 | -4.138 | -8.42987 | -1.03525 |
| user_006 | Standing | 1.446046941159e12 | -4.09967 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046941191e12 | -4.06135 | -8.65979 | -1.07357 |
| user_006 | Standing | 1.446046941224e12 | -4.138 | -8.88971 | -1.11189 |
| user_006 | Standing | 1.446046941264e12 | -4.25296 | -8.73643 | -1.03525 |
| user_006 | Standing | 1.446046941305e12 | -4.17632 | -8.73643 | -1.11189 |
| user_006 | Standing | 1.446046941329e12 | -4.25296 | -8.58315 | -1.18853 |
| user_006 | Standing | 1.446046941337e12 | -4.3296 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046941426e12 | -4.21464 | -8.58315 | -0.920286 |
| user_006 | Standing | 1.446046941458e12 | -4.138 | -8.77475 | -1.15021 |
| user_006 | Standing | 1.446046941482e12 | -4.25296 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046941547e12 | -4.3296 | -8.65979 | -1.18853 |
| user_006 | Standing | 1.446046941636e12 | -4.21464 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.44604694175e12 | -4.36792 | -8.58315 | -1.22685 |
| user_006 | Standing | 1.446046942081e12 | -4.17632 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.446046942534e12 | -4.17632 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046942923e12 | -4.25296 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046943118e12 | -4.17632 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046944606e12 | -4.138 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.44604694467e12 | -4.138 | -8.73643 | -0.958607 |
| user_002 | Standing | 1.446034733566e12 | -1.49389 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446034734085e12 | -1.37893 | -9.38788 | 0.229323 |
| user_002 | Standing | 1.446034734797e12 | -1.53221 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446034735056e12 | -1.37893 | -9.38788 | 3.7722e-2 |
| user_002 | Standing | 1.446034735508e12 | -1.37893 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446034735703e12 | -1.45557 | -9.4262 | 0.267643 |
| user_002 | Standing | 1.446034736285e12 | -1.37893 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446034737257e12 | -1.49389 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446034738228e12 | -1.37893 | -9.4262 | 0.152682 |
| user_002 | Standing | 1.446034738876e12 | -1.37893 | -9.50284 | 0.229323 |
| user_002 | Standing | 1.446034739004e12 | -1.45557 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034739134e12 | -1.30229 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034739264e12 | -1.41725 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034739523e12 | -1.34061 | -9.54116 | 0.229323 |
| user_002 | Standing | 1.44603474043e12 | -1.34061 | -9.50284 | 0.191003 |
| user_002 | Standing | 1.446034741335e12 | -1.34061 | -9.46452 | 0.114362 |
| user_002 | Standing | 1.446034742123e12 | -1.37893 | -9.46452 | 3.7722e-2 |
| user_002 | Standing | 1.446034742237e12 | -1.37893 | -9.54116 | 0.191003 |
| user_002 | Standing | 1.44603474231e12 | -1.49389 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034742334e12 | -1.45557 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446034742383e12 | -1.34061 | -9.4262 | 7.6042e-2 |
| user_002 | Standing | 1.446034742424e12 | -1.49389 | -9.54116 | 0.191003 |
| user_002 | Standing | 1.446034742488e12 | -1.37893 | -9.38788 | 0.152682 |
| user_002 | Standing | 1.446034742697e12 | -1.41725 | -9.54116 | 0.229323 |
| user_002 | Standing | 1.446034742916e12 | -1.26397 | -9.4262 | 0.114362 |
| user_002 | Standing | 1.446034742932e12 | -0.995729 | -9.34956 | 0.344284 |
| user_002 | Standing | 1.446034743369e12 | -1.37893 | -9.57948 | 0.114362 |
| user_002 | Standing | 1.446034743386e12 | -1.26397 | -9.38788 | 0.344284 |
| user_002 | Standing | 1.446034743434e12 | -1.18733 | -9.46452 | 0.344284 |
| user_002 | Standing | 1.446034743499e12 | -1.41725 | -9.31124 | 0.344284 |
| user_002 | Standing | 1.44603474371e12 | -1.26397 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.44603474375e12 | -1.22565 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446034743791e12 | -1.22565 | -9.46452 | 0.229323 |
| user_002 | Standing | 1.446034743798e12 | -1.18733 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.446034743823e12 | -1.45557 | -9.27292 | 3.7722e-2 |
| user_002 | Standing | 1.446034743863e12 | -1.26397 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034744001e12 | -1.30229 | -9.57948 | 0.267643 |
| user_002 | Standing | 1.446034744025e12 | -1.18733 | -9.4262 | 0.305964 |
| user_002 | Standing | 1.446034744074e12 | -1.22565 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034744098e12 | -1.22565 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034744171e12 | -1.18733 | -9.54116 | 0.305964 |
| user_002 | Standing | 1.446034744203e12 | -1.18733 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446034744211e12 | -1.22565 | -9.38788 | 0.229323 |
| user_002 | Standing | 1.446034744236e12 | -1.22565 | -9.27292 | 0.344284 |
| user_002 | Standing | 1.446034744325e12 | -1.34061 | -9.4262 | 0.305964 |
| user_002 | Standing | 1.446034744365e12 | -1.22565 | -9.38788 | 0.229323 |
| user_002 | Standing | 1.446034744551e12 | -1.41725 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446034744559e12 | -1.41725 | -9.38788 | 0.459245 |
| user_002 | Standing | 1.446034744632e12 | -1.30229 | -9.46452 | 0.420925 |
| user_002 | Standing | 1.446034744665e12 | -1.11069 | -9.34956 | 0.420925 |
| user_002 | Standing | 1.446034744738e12 | -1.30229 | -9.4262 | 0.382604 |
| user_002 | Standing | 1.446034744964e12 | -1.26397 | -9.4262 | 0.229323 |
| user_002 | Standing | 1.446034744981e12 | -1.30229 | -9.38788 | -3.8919e-2 |
| user_002 | Standing | 1.446034745045e12 | -1.22565 | -9.6178 | 0.305964 |
| user_002 | Standing | 1.446034745191e12 | -0.880768 | -9.46452 | 0.344284 |
| user_002 | Standing | 1.446034745215e12 | -1.26397 | -9.57948 | 0.114362 |
| user_002 | Standing | 1.446034745345e12 | -1.37893 | -9.46452 | 0.420925 |
| user_002 | Standing | 1.446034745369e12 | -1.14901 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034745385e12 | -1.30229 | -9.46452 | 0.152682 |
| user_002 | Standing | 1.44603474545e12 | -1.18733 | -9.38788 | 0.152682 |
| user_002 | Standing | 1.446034745482e12 | -1.41725 | -9.54116 | 0.229323 |
| user_002 | Standing | 1.44603474549e12 | -1.26397 | -9.65612 | 0.420925 |
| user_002 | Standing | 1.446034745507e12 | -1.41725 | -9.46452 | 0.114362 |
| user_002 | Standing | 1.446034745636e12 | -1.30229 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446034745685e12 | -1.45557 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034745741e12 | -1.18733 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034745863e12 | -1.26397 | -9.57948 | 0.152682 |
| user_002 | Standing | 1.446034746082e12 | -1.30229 | -9.38788 | 0.305964 |
| user_002 | Standing | 1.446034746858e12 | -1.34061 | -9.4262 | 0.305964 |
| user_002 | Standing | 1.446034747311e12 | -1.30229 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034748736e12 | -1.18733 | -9.46452 | 0.267643 |
| user_002 | Standing | 1.446034749383e12 | -1.18733 | -9.46452 | 0.267643 |
| user_002 | Standing | 1.446146630001e12 | -4.02303 | -8.73643 | -0.920286 |
| user_002 | Standing | 1.446146631036e12 | -3.83143 | -8.69811 | -1.11189 |
| user_002 | Standing | 1.446146631166e12 | -4.09967 | -8.62147 | -1.03525 |
| user_002 | Standing | 1.446146632655e12 | -4.59784 | -7.70178 | -2.22318 |
| user_002 | Standing | 1.446146633757e12 | -0.267643 | -10.65245 | 1.07237 |
| user_002 | Standing | 1.446146634987e12 | -0.267643 | -9.92436 | 0.689167 |
| user_002 | Standing | 1.446146635375e12 | -1.60885 | -8.50651 | 1.57053 |
| user_002 | Standing | 1.446146635505e12 | -1.49389 | -9.11964 | 2.41358 |
| user_002 | Standing | 1.446146635635e12 | -1.99206 | -9.11964 | 1.14901 |
| user_002 | Standing | 1.446146635828e12 | -1.26397 | -9.19628 | 1.49389 |
| user_002 | Standing | 1.446146637577e12 | -1.45557 | -9.2346 | 1.57053 |
| user_002 | Standing | 1.44614663803e12 | -0.382604 | -10.11596 | 1.57053 |
| user_002 | Standing | 1.446146640036e12 | -0.229323 | -9.08132 | 2.03038 |
| user_002 | Standing | 1.446146641072e12 | -0.114362 | -9.00468 | 2.10702 |
| user_002 | Standing | 1.446146641267e12 | 5.99e-4 | -9.15796 | 2.14534 |
| user_002 | Standing | 1.446146642043e12 | -7.6042e-2 | -9.15796 | 2.22198 |
| user_002 | Standing | 1.446146644825e12 | 3.37279 | -8.62147 | 1.45557 |
| user_002 | Standing | 1.446146645409e12 | 3.83263 | -8.54483 | 1.30229 |
| user_002 | Walking | 1.446034820068e12 | -3.98471 | -8.81307 | 2.49022 |
| user_002 | Walking | 1.446034821104e12 | -4.138 | -7.85507 | 1.72382 |
| user_002 | Walking | 1.446034821558e12 | -3.29495 | -9.34956 | 2.75846 |
| user_002 | Walking | 1.446034822592e12 | -4.21464 | -8.27659 | 2.8351 |
| user_002 | Walking | 1.446034822787e12 | -4.94272 | -8.65979 | 1.22565 |
| user_002 | Walking | 1.446034823953e12 | -5.36425 | -9.50284 | 2.14534 |
| user_002 | Walking | 1.446034824277e12 | -3.98471 | -7.05034 | 2.10702 |
| user_002 | Walking | 1.446034824406e12 | -3.44823 | -6.28393 | -3.8919e-2 |
| user_002 | Walking | 1.446034824665e12 | -5.55585 | -8.85139 | 1.45557 |
| user_002 | Walking | 1.446034826609e12 | -4.55952 | -11.18893 | 1.18733 |
| user_002 | Walking | 1.446034827386e12 | -3.98471 | -7.89339 | 2.18366 |
| user_002 | Walking | 1.446034827451e12 | -3.75479 | -8.50651 | 1.91542 |
| user_002 | Walking | 1.44603482758e12 | -5.74745 | -7.58682 | 2.79678 |
| user_002 | Walking | 1.446034828098e12 | -4.44456 | -6.20729 | -1.76333 |
| user_002 | Walking | 1.446034828746e12 | -4.98104 | -7.39522 | 2.0687 |
| user_002 | Walking | 1.446034828811e12 | -5.90073 | -7.7401 | 2.33694 |
| user_002 | Walking | 1.446034829458e12 | -7.77842 | -11.53382 | -3.10454 |
| user_002 | Walking | 1.446034829523e12 | -6.36057 | -9.27292 | 0.114362 |
| user_002 | Walking | 1.44603483043e12 | -3.14167 | -6.39889 | 0.344284 |
| user_002 | Walking | 1.44603483056e12 | -3.29495 | -6.28393 | -1.03525 |
| user_002 | Walking | 1.446034832438e12 | -5.32592 | -7.3569 | 1.83878 |
| user_002 | Walking | 1.446034834769e12 | -4.29128 | -8.00835 | 1.57053 |
| user_002 | Walking | 1.446034836e12 | -4.48288 | -7.70178 | 1.60885 |
| user_005 | Walking | 1.446046499672e12 | -4.78944 | -11.30389 | -6.28513 |
| user_005 | Walking | 1.446046499736e12 | -1.91542 | -13.948 | -0.230521 |
| user_005 | Walking | 1.446046500255e12 | -5.36425 | -12.49182 | -1.45677 |
| user_005 | Walking | 1.446046501161e12 | -2.4519 | -8.39155 | 0.804128 |
| user_005 | Walking | 1.446046501679e12 | -3.02671 | -5.40257 | 0.612526 |
| user_005 | Walking | 1.446046501873e12 | 1.22685 | -15.25089 | -12.30142 |
| user_005 | Walking | 1.446046502197e12 | -0.842448 | -7.24194 | -0.15388 |
| user_005 | Walking | 1.446046502261e12 | -0.919089 | -8.16163 | 0.880768 |
| user_005 | Walking | 1.446046502455e12 | -4.7128 | -11.18893 | -0.652044 |
| user_005 | Walking | 1.446046502908e12 | 0.767005 | -11.45717 | -10.65365 |
| user_005 | Walking | 1.446046503103e12 | 3.8919e-2 | -5.86241 | 3.60151 |
| user_005 | Walking | 1.446046503362e12 | -1.53221 | -9.00468 | -1.83997 |
| user_005 | Walking | 1.446046504528e12 | -3.56319 | -11.18893 | -0.958607 |
| user_005 | Walking | 1.446046505304e12 | -3.60151 | -10.92069 | 2.8351 |
| user_005 | Walking | 1.44604650757e12 | -3.94639 | -12.14694 | 7.6042e-2 |
| user_005 | Walking | 1.446046507635e12 | 0.996927 | -7.47186 | -1.41845 |
| user_005 | Walking | 1.446046508023e12 | 0.422122 | -11.99366 | 0.459245 |
| user_005 | Walking | 1.446046508476e12 | 2.87462 | -10.15428 | -8.1245 |
| user_005 | Walking | 1.44604650867e12 | -3.17999 | -11.07397 | 0.727487 |
| user_005 | Walking | 1.446046508864e12 | -1.18733 | -8.35323 | -1.03525 |
| user_005 | Walking | 1.446046508928e12 | -2.29862 | -9.65612 | -0.805325 |
| user_005 | Walking | 1.446046509123e12 | 1.34181 | -11.15061 | 0.344284 |
| user_005 | Walking | 1.446046509512e12 | 1.45677 | -8.50651 | -6.85994 |
| user_005 | Walking | 1.446046510224e12 | -7.6042e-2 | -11.45717 | -7.7239e-2 |
| user_005 | Walking | 1.446046510288e12 | 1.22685 | -11.64878 | 1.22565 |
| user_005 | Walking | 1.446046510676e12 | 2.68302 | -11.4955 | -9.46572 |
| user_005 | Walking | 1.446046511648e12 | -2.03038 | -6.28393 | -0.307161 |
| user_005 | Walking | 1.446046511971e12 | 5.21216 | -13.44983 | -0.690364 |
| user_005 | Walking | 1.446046512618e12 | 2.68302 | -13.10495 | 0.689167 |
| user_005 | Walking | 1.446046513653e12 | -0.191003 | -11.26557 | 0.574206 |
| user_005 | Walking | 1.446046514171e12 | 4.5224 | -8.77475 | -7.70298 |
| user_005 | Walking | 1.446046514883e12 | 2.2615 | -12.4535 | 0.574206 |
| user_005 | Walking | 1.446046515336e12 | 3.64103 | -8.73643 | -6.20849 |
| user_005 | Walking | 1.446046515789e12 | -2.03038 | -10.84405 | -0.958607 |
| user_005 | Walking | 1.446046515919e12 | 0.767005 | -11.15061 | 0.191003 |
| user_005 | Walking | 1.446046516049e12 | 3.48775 | -14.21624 | 0.880768 |
| user_005 | Walking | 1.446046516437e12 | 5.17384 | -9.57948 | -8.66099 |
| user_005 | Walking | 1.446046516501e12 | 6.05521 | -12.22358 | -2.18486 |
| user_002 | Jogging | 1.446034899761e12 | -19.27452 | -8.12331 | -6.93658 |
| user_002 | Jogging | 1.446034900473e12 | -16.05561 | -9.34956 | -4.10087 |
| user_002 | Jogging | 1.446034901249e12 | -16.66874 | -11.99366 | -6.9749 |
| user_002 | Jogging | 1.446034901357e12 | -4.06135 | -2.98839 | 2.14534 |
| user_002 | Jogging | 1.446034901365e12 | -2.22198 | -2.60518 | 1.91542 |
| user_002 | Jogging | 1.446034901438e12 | 4.5224 | -0.382604 | -2.14654 |
| user_002 | Jogging | 1.446034901495e12 | -0.535886 | -4.06135 | 0.305964 |
| user_002 | Jogging | 1.446034901576e12 | -17.12858 | -14.906 | 4.86608 |
| user_002 | Jogging | 1.446034901624e12 | -13.06663 | -16.7837 | -0.920286 |
| user_002 | Jogging | 1.446034901883e12 | -3.06503 | 7.7239e-2 | -0.11556 |
| user_002 | Jogging | 1.446034901908e12 | -0.459245 | -1.8771 | 5.01936 |
| user_002 | Jogging | 1.44603490194e12 | -4.82776 | -13.60311 | -5.59536 |
| user_002 | Jogging | 1.446034901996e12 | -18.31651 | -13.29655 | -8.89091 |
| user_002 | Jogging | 1.446034902125e12 | -6.16897 | -1.99206 | -0.422122 |
| user_002 | Jogging | 1.446034902167e12 | -0.995729 | 3.48775 | -0.996927 |
| user_002 | Jogging | 1.446034902191e12 | 1.83997 | 3.14286 | -1.41845 |
| user_002 | Jogging | 1.446034902247e12 | -3.98471 | -2.22198 | -5.99e-4 |
| user_002 | Jogging | 1.446034902256e12 | -3.83143 | -3.06503 | 0.765807 |
| user_002 | Jogging | 1.446034902563e12 | -6.13065 | 2.56806 | -0.843646 |
| user_002 | Jogging | 1.446034902741e12 | -13.37319 | -12.18526 | -5.25048 |
| user_002 | Jogging | 1.446034902766e12 | -15.25089 | -14.1396 | -7.01322 |
| user_002 | Jogging | 1.446034903016e12 | 1.72501 | -3.75479 | -1.61005 |
| user_002 | Jogging | 1.446034903219e12 | -13.06663 | -9.92436 | -0.498763 |
| user_002 | Jogging | 1.446034903421e12 | 0.460442 | -3.37159 | 1.91542 |
| user_002 | Jogging | 1.446034903445e12 | -2.49022 | -3.79311 | 0.497565 |
| user_002 | Jogging | 1.446034903461e12 | -6.36057 | -10.92069 | -1.26517 |
| user_002 | Jogging | 1.446034903518e12 | -15.02096 | -18.31651 | -6.63001 |
| user_002 | Jogging | 1.446034903534e12 | -17.62674 | -16.55378 | -8.16283 |
| user_002 | Jogging | 1.446034903542e12 | -18.92964 | -14.59944 | -8.73763 |
| user_002 | Jogging | 1.446034903639e12 | -5.86241 | -2.87342 | 0.191003 |
| user_002 | Jogging | 1.446034903712e12 | 2.8363 | 2.2615 | -3.2195 |
| user_002 | Jogging | 1.446034903769e12 | -3.7722e-2 | -2.4519 | -4.67568 |
| user_002 | Jogging | 1.446034903785e12 | -8.16163 | -1.14901 | -1.64837 |
| user_002 | Jogging | 1.446034903793e12 | -8.00835 | -2.87342 | -1.18853 |
| user_002 | Jogging | 1.446034903809e12 | -10.1926 | -4.17632 | -3.0279 |
| user_002 | Jogging | 1.446034903915e12 | -18.46979 | -11.11229 | -5.63368 |
| user_002 | Jogging | 1.446034904077e12 | -2.98839 | 2.03158 | -0.613724 |
| user_002 | Jogging | 1.446034904085e12 | -2.49022 | 3.52607 | -1.11189 |
| user_002 | Jogging | 1.446034904384e12 | -9.6178 | -4.59784 | -5.05888 |
| user_002 | Jogging | 1.446034904497e12 | 4.75232 | 0.498763 | -3.18118 |
| user_002 | Jogging | 1.446034904554e12 | -3.60151 | -0.612526 | -0.996927 |
| user_002 | Jogging | 1.44603490457e12 | -5.63249 | -2.18366 | 0.497565 |
| user_002 | Jogging | 1.446034904797e12 | -9.08132 | -7.51018 | -1.76333 |
| user_002 | Jogging | 1.446034904813e12 | -5.82409 | -7.24194 | -0.690364 |
| user_002 | Jogging | 1.446034904935e12 | -0.727487 | -3.02671 | -0.537083 |
| user_002 | Jogging | 1.446034905144e12 | -11.11229 | -0.919089 | -4.40743 |
| user_002 | Jogging | 1.446034905266e12 | 6.17017 | -2.49022 | -0.460442 |
| user_002 | Jogging | 1.446034905331e12 | -5.97737 | -3.83143 | 3.7722e-2 |
| user_002 | Jogging | 1.446034905469e12 | -15.55745 | -16.05561 | 1.76214 |
| user_002 | Jogging | 1.446034905606e12 | -4.17632 | 0.11556 | -2.52974 |
| user_002 | Jogging | 1.446034905631e12 | -1.34061 | 4.40743 | -4.13919 |
| user_002 | Jogging | 1.446034905736e12 | 0.498763 | -10.23092 | 0.267643 |
| user_002 | Jogging | 1.446034905971e12 | 3.25783 | 1.53341 | 1.30229 |
| user_002 | Jogging | 1.446034906003e12 | 6.17017 | 0.11556 | -1.61005 |
| user_002 | Jogging | 1.446034906319e12 | -3.56319 | -4.98104 | -1.22685 |
| user_002 | Jogging | 1.446034906327e12 | -4.21464 | -4.59784 | -1.91661 |
| user_002 | Jogging | 1.446034906383e12 | -3.29495 | 3.44943 | -5.55704 |
| user_002 | Jogging | 1.446034906408e12 | -3.37159 | -2.6435 | -0.537083 |
| user_002 | Jogging | 1.446034906578e12 | -17.32018 | -10.57581 | -11.76493 |
| user_002 | Jogging | 1.446034906667e12 | -8.81307 | -0.842448 | -3.29615 |
| user_002 | Jogging | 1.446034906683e12 | -5.74745 | -0.191003 | -1.38013 |
| user_002 | Jogging | 1.446034906699e12 | -1.60885 | -0.459245 | 0.114362 |
| user_002 | Jogging | 1.446034906707e12 | 0.268841 | -0.535886 | 0.382604 |
| user_002 | Jogging | 1.446034906731e12 | 3.44943 | 1.22685 | -1.80165 |
| user_002 | Jogging | 1.44603490682e12 | -6.13065 | -4.36792 | -0.11556 |
| user_002 | Jogging | 1.446034906989e12 | -18.31651 | -13.06663 | 0.650847 |
| user_002 | Jogging | 1.446034907063e12 | -8.19995 | -4.44456 | -2.10822 |
| user_002 | Jogging | 1.446034907071e12 | -7.01202 | -3.33327 | -1.45677 |
| user_002 | Jogging | 1.446034907088e12 | -4.25296 | -3.14167 | -1.26517 |
| user_002 | Jogging | 1.446034907161e12 | -3.33327 | 4.67568 | -4.29247 |
| user_002 | Jogging | 1.446034907217e12 | -2.60518 | -3.44823 | -0.1922 |
| user_002 | Jogging | 1.446034907233e12 | -2.87342 | -2.18366 | 2.03038 |
| user_002 | Jogging | 1.44603490729e12 | -14.5228 | -14.98264 | -4.714 |
| user_002 | Jogging | 1.446034907314e12 | -18.69972 | -13.60311 | -8.77595 |
| user_002 | Jogging | 1.446034907395e12 | -17.39682 | -3.48655 | -7.31978 |
| user_002 | Jogging | 1.446034907426e12 | -10.15428 | -2.03038 | -4.21583 |
| user_002 | Jogging | 1.446034907451e12 | -7.47186 | -1.8771 | -2.18486 |
| user_002 | Jogging | 1.446034907694e12 | -18.54643 | -8.50651 | 2.72014 |
| user_002 | Jogging | 1.446034907759e12 | -15.86401 | -13.71807 | 0.919089 |
| user_002 | Jogging | 1.446034907767e12 | -16.55378 | -13.948 | 0.804128 |
| user_002 | Jogging | 1.446034908074e12 | -9.69444 | -11.8787 | -0.805325 |
| user_002 | Jogging | 1.446034908721e12 | -3.86975 | 2.87462 | -4.36911 |
| user_002 | Jogging | 1.4460349106e12 | -13.44983 | -4.44456 | -6.55337 |
| user_002 | Jogging | 1.446034911117e12 | -4.138 | 1.57173 | -2.87462 |
| user_002 | Jogging | 1.446034911571e12 | 4.13919 | -1.03405 | -1.87829 |
| user_002 | Jogging | 1.446034912607e12 | -7.43354 | -7.62514 | -2.56806 |
| user_002 | Jogging | 1.446034913189e12 | -7.77842 | -5.78577 | -1.83997 |
| user_002 | Jogging | 1.446034914678e12 | -3.67815 | 0.345482 | 2.2603 |
| user_002 | Jogging | 1.446034915714e12 | -12.68342 | -12.56846 | 1.37893 |
| user_005 | Jumping | 1.446046630501e12 | -4.29128 | 1.07357 | -2.41478 |
| user_005 | Jumping | 1.446046630824e12 | -0.267643 | -3.79311 | 7.6042e-2 |
| user_005 | Jumping | 1.446046631278e12 | 0.383802 | 1.53341 | -1.03525 |
| user_005 | Jumping | 1.446046631407e12 | -1.8771 | -1.8771 | 0.612526 |
| user_005 | Jumping | 1.446046632184e12 | -0.344284 | -8.27659 | -5.32712 |
| user_005 | Jumping | 1.446046632766e12 | -2.75846 | -3.60151 | 1.37893 |
| user_005 | Jumping | 1.446046632832e12 | 0.537083 | -6.93538 | -4.5224 |
| user_005 | Jumping | 1.446046634321e12 | -1.45557 | -15.28921 | -1.15021 |
| user_005 | Jumping | 1.446046634515e12 | 1.87829 | 2.03158 | 3.21831 |
| user_005 | Jumping | 1.446046634839e12 | 4.94392 | -18.92964 | -10.99853 |
| user_005 | Jumping | 1.446046635615e12 | 1.99325 | -16.01729 | -1.11189 |
| user_005 | Jumping | 1.446046636069e12 | 0.537083 | -2.95007 | 3.7722e-2 |
| user_005 | Jumping | 1.446046637039e12 | -1.37893 | -0.689167 | -7.58802 |
| user_005 | Jumping | 1.446046637493e12 | 9.69564 | -19.58108 | -10.61533 |
| user_005 | Jumping | 1.446046638271e12 | 4.82896 | -17.28186 | -16.82322 |
| user_005 | Jumping | 1.446046638465e12 | 0.15388 | -1.8771 | 2.0687 |
| user_005 | Jumping | 1.446046639695e12 | 7.85626 | -19.54276 | -7.12818 |
| user_005 | Jumping | 1.44604663976e12 | -0.880768 | -7.1653 | -0.11556 |
| user_005 | Jumping | 1.446046640018e12 | -1.57053 | 1.45677 | -1.57173 |
| user_005 | Jumping | 1.446046640277e12 | 3.25783 | -19.58108 | -10.76861 |
| user_005 | Jumping | 1.446046640342e12 | 6.78329 | -19.35116 | -4.63736 |
| user_005 | Jumping | 1.446046640471e12 | -1.03405 | -0.420925 | -3.67935 |
| user_005 | Jumping | 1.446046640601e12 | -0.114362 | 0.345482 | -0.958607 |
| user_005 | Jumping | 1.44604664112e12 | -1.8771 | 0.460442 | -3.94759 |
| user_005 | Jumping | 1.44604664209e12 | 3.0279 | -10.53749 | -5.90193 |
| user_005 | Jumping | 1.44604664235e12 | -2.68182 | -3.7722e-2 | -3.60271 |
| user_005 | Jumping | 1.446046642674e12 | -2.03038 | -2.98839 | 1.37893 |
| user_005 | Jumping | 1.446046642803e12 | 5.17384 | -19.58108 | -7.05154 |
| user_005 | Jumping | 1.446046644551e12 | 1.30349 | -2.22198 | 2.29862 |
| user_005 | Jumping | 1.44604664494e12 | 6.78329 | -14.79104 | -10.3854 |
| user_005 | Jumping | 1.446046645522e12 | -0.765807 | -1.37893 | 1.45557 |
| user_001 | Walking | 1.446047180025e12 | 3.48775 | -7.70178 | 0.420925 |
| user_001 | Walking | 1.446047180219e12 | 1.83997 | -6.09233 | -0.15388 |
| user_001 | Walking | 1.446047180672e12 | 4.21583 | -5.93905 | 7.6042e-2 |
| user_001 | Walking | 1.446047180737e12 | 2.75966 | -6.36057 | 0.344284 |
| user_001 | Walking | 1.446047181838e12 | 4.25415 | -6.89706 | 1.18733 |
| user_001 | Walking | 1.446047183131e12 | 2.14654 | -7.01202 | -0.1922 |
| user_001 | Walking | 1.446047183391e12 | 6.13185 | -8.42987 | 0.880768 |
| user_001 | Walking | 1.446047186239e12 | 3.10454 | -11.84038 | 2.0687 |
| user_001 | Walking | 1.446047186563e12 | 2.87462 | -7.51018 | 0.191003 |
| user_001 | Walking | 1.446047187275e12 | 11.99486 | -10.46085 | -0.498763 |
| user_001 | Walking | 1.446047187792e12 | 4.56072 | -8.81307 | 0.382604 |
| user_001 | Walking | 1.446047188634e12 | -2.14534 | -11.64878 | 1.99206 |
| user_001 | Walking | 1.446047188699e12 | 0.958607 | -8.96635 | 1.91542 |
| user_001 | Walking | 1.446047189022e12 | 4.98224 | -11.26557 | 2.29862 |
| user_001 | Walking | 1.446047190317e12 | 3.56439 | -9.38788 | 3.06503 |
| user_001 | Walking | 1.446047190835e12 | 4.86728 | -12.91335 | 4.25296 |
| user_001 | Walking | 1.446047191093e12 | 0.575403 | -7.62514 | 2.0687 |
| user_001 | Walking | 1.446047191677e12 | 0.537083 | -8.50651 | -0.307161 |
| user_001 | Walking | 1.446047191742e12 | -0.344284 | -5.82409 | 0.382604 |
| user_001 | Walking | 1.446047191871e12 | -0.765807 | -7.08866 | 0.842448 |
| user_001 | Walking | 1.446047192195e12 | -0.765807 | -10.34589 | 1.8771 |
| user_001 | Walking | 1.446047192518e12 | 3.41111 | -8.85139 | 3.60151 |
| user_001 | Walking | 1.446047192713e12 | 3.48775 | -8.46819 | 2.72014 |
| user_001 | Walking | 1.446047192907e12 | 7.7239e-2 | -8.16163 | 0.420925 |
| user_001 | Walking | 1.446047193166e12 | 6.66833 | -9.57948 | 0.344284 |
| user_001 | Walking | 1.44604719336e12 | 2.37646 | -8.73643 | -0.537083 |
| user_001 | Walking | 1.446047194461e12 | 7.01322 | -12.37686 | 0.305964 |
| user_001 | Walking | 1.446047194979e12 | 5.71033 | -10.57581 | 0.880768 |
| user_001 | Walking | 1.446047195367e12 | -0.689167 | -5.70913 | 1.41725 |
| user_001 | Walking | 1.446047196144e12 | 5.94025 | -9.96268 | 1.07237 |
| user_002 | Jumping | 1.446035034445e12 | -1.76214 | -0.267643 | -0.996927 |
| user_002 | Jumping | 1.446035035351e12 | -14.75272 | -19.46612 | 0.995729 |
| user_002 | Jumping | 1.44603503574e12 | -1.68549 | -2.10702 | -1.95493 |
| user_002 | Jumping | 1.446035036129e12 | -0.114362 | 0.996927 | -3.8919e-2 |
| user_002 | Jumping | 1.446035036582e12 | -10.80573 | -14.94432 | 1.22565 |
| user_002 | Jumping | 1.446035036647e12 | -0.459245 | -2.14534 | -0.767005 |
| user_002 | Jumping | 1.44603503736e12 | 0.268841 | -0.995729 | -0.958607 |
| user_002 | Jumping | 1.446035038783e12 | -9.88604 | -13.48815 | 0.574206 |
| user_002 | Jumping | 1.446035040338e12 | 5.99e-4 | -1.07237 | -0.1922 |
| user_002 | Jumping | 1.446035040531e12 | -9.65612 | -12.60678 | -1.15021 |
| user_002 | Jumping | 1.446035043056e12 | -12.41518 | -19.08292 | -2.2615 |
| user_002 | Jumping | 1.446035043121e12 | -4.3296 | -6.74378 | 0.114362 |
| user_002 | Jumping | 1.446035043962e12 | -1.76214 | -0.919089 | -1.38013 |
| user_002 | Jumping | 1.446035044415e12 | 0.268841 | 1.68669 | -1.57173 |
| user_002 | Jumping | 1.446035044609e12 | -1.41725 | -1.45557 | -1.38013 |
| user_002 | Jumping | 1.446035044868e12 | -11.57214 | -16.5921 | -0.498763 |
| user_002 | Jumping | 1.446035046228e12 | -7.6042e-2 | 0.307161 | -7.7239e-2 |
| user_002 | Jumping | 1.446035046486e12 | -13.21991 | -15.71073 | 0.305964 |
| user_002 | Jumping | 1.446035047263e12 | -4.06135 | -5.90073 | -3.8919e-2 |
| user_002 | Jumping | 1.446035048234e12 | -12.83671 | -15.0976 | 7.6042e-2 |
| user_002 | Jumping | 1.446035049399e12 | -13.52647 | -17.3585 | -0.958607 |
| user_003 | Sitting | 1.446047526718e12 | 0.422122 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047527172e12 | 0.498763 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047528661e12 | 0.460442 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047529656e12 | 0.460442 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.446047529705e12 | 0.422122 | -0.650847 | 9.69444 |
| user_003 | Sitting | 1.446047529786e12 | 0.345482 | -0.727487 | 9.34956 |
| user_003 | Sitting | 1.446047529817e12 | 0.575403 | -0.612526 | 9.34956 |
| user_003 | Sitting | 1.446047529842e12 | 0.498763 | -0.650847 | 9.31124 |
| user_003 | Sitting | 1.44604752985e12 | 0.460442 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.446047529988e12 | 0.307161 | -0.804128 | 9.38788 |
| user_003 | Sitting | 1.446047530045e12 | 0.460442 | -0.804128 | 9.4262 |
| user_003 | Sitting | 1.446047530101e12 | 0.345482 | -0.574206 | 9.4262 |
| user_003 | Sitting | 1.446047530133e12 | 0.345482 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047530215e12 | 0.537083 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047530231e12 | 0.460442 | -0.612526 | 9.54116 |
| user_003 | Sitting | 1.446047530255e12 | 0.383802 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047530263e12 | 0.537083 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047530425e12 | 0.575403 | -0.574206 | 9.50284 |
| user_003 | Sitting | 1.446047530562e12 | 0.575403 | -0.765807 | 9.46452 |
| user_003 | Sitting | 1.446047530595e12 | 0.345482 | -0.689167 | 9.31124 |
| user_003 | Sitting | 1.44604753061e12 | 0.575403 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.4460475307e12 | 0.575403 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047530707e12 | 0.613724 | -0.727487 | 9.38788 |
| user_003 | Sitting | 1.446047530749e12 | 0.422122 | -0.765807 | 9.4262 |
| user_003 | Sitting | 1.446047530846e12 | 0.383802 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.446047530871e12 | 0.575403 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047530886e12 | 0.537083 | -0.650847 | 9.34956 |
| user_003 | Sitting | 1.446047530927e12 | 0.460442 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047531113e12 | 0.422122 | -0.765807 | 9.50284 |
| user_003 | Sitting | 1.44604753121e12 | 0.383802 | -0.497565 | 9.34956 |
| user_003 | Sitting | 1.446047531217e12 | 0.383802 | -0.612526 | 9.38788 |
| user_003 | Sitting | 1.446047531234e12 | 0.422122 | -0.612526 | 9.38788 |
| user_003 | Sitting | 1.446047531493e12 | 0.537083 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047531526e12 | 0.307161 | -0.574206 | 9.46452 |
| user_003 | Sitting | 1.446047531656e12 | 0.498763 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047531728e12 | 0.230521 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047531736e12 | 0.307161 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047531768e12 | 0.460442 | -0.765807 | 9.50284 |
| user_003 | Sitting | 1.446047531833e12 | 0.537083 | -0.880768 | 9.38788 |
| user_003 | Sitting | 1.446047531881e12 | 0.498763 | -0.612526 | 9.38788 |
| user_003 | Sitting | 1.446047531889e12 | 0.498763 | -0.765807 | 9.4262 |
| user_003 | Sitting | 1.446047531922e12 | 0.383802 | -0.765807 | 9.38788 |
| user_003 | Sitting | 1.44604753193e12 | 0.460442 | -0.880768 | 9.46452 |
| user_003 | Sitting | 1.446047531939e12 | 0.460442 | -0.612526 | 9.54116 |
| user_003 | Sitting | 1.446047532027e12 | 0.422122 | -0.842448 | 9.38788 |
| user_003 | Sitting | 1.446047532043e12 | 0.460442 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047532108e12 | 0.575403 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047532237e12 | 0.422122 | -0.574206 | 9.50284 |
| user_003 | Sitting | 1.446047532724e12 | 0.460442 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.44604753278e12 | 0.460442 | -0.535886 | 9.34956 |
| user_003 | Sitting | 1.446047533443e12 | 0.498763 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047533702e12 | 0.422122 | -0.650847 | 9.50284 |
| user_003 | Sitting | 1.446047535127e12 | 0.422122 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047535386e12 | 0.498763 | -0.689167 | 9.38788 |
| user_003 | Sitting | 1.446047535904e12 | 0.422122 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047536033e12 | 0.460442 | -0.650847 | 9.50284 |
| user_003 | Sitting | 1.446047536227e12 | 0.422122 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047536745e12 | 0.460442 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047537781e12 | 0.460442 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047537963e12 | 2.91294 | -4.06135 | -17.7429 |
| user_003 | Sitting | 1.44604753806e12 | 0.460442 | -0.727487 | 9.34956 |
| user_003 | Sitting | 1.446047538237e12 | 0.537083 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047538246e12 | 0.690364 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.44604753827e12 | 0.498763 | -0.689167 | 9.54116 |
| user_003 | Sitting | 1.446047538287e12 | 0.460442 | -0.842448 | 9.54116 |
| user_003 | Sitting | 1.446047538295e12 | 0.537083 | -0.727487 | 9.57948 |
| user_003 | Sitting | 1.446047538343e12 | 0.537083 | -0.574206 | 9.38788 |
| user_003 | Sitting | 1.446047538448e12 | 0.345482 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047538529e12 | 0.460442 | -0.650847 | 9.34956 |
| user_003 | Sitting | 1.446047538577e12 | 0.383802 | -0.804128 | 9.34956 |
| user_003 | Sitting | 1.44604753861e12 | 0.383802 | -0.574206 | 9.46452 |
| user_003 | Sitting | 1.446047538626e12 | 0.498763 | -0.727487 | 9.38788 |
| user_003 | Sitting | 1.446047538804e12 | 0.613724 | -0.765807 | 9.2346 |
| user_003 | Sitting | 1.446047538846e12 | 0.498763 | -0.612526 | 9.31124 |
| user_003 | Sitting | 1.446047539161e12 | 0.537083 | -0.574206 | 9.6178 |
| user_003 | Sitting | 1.44604753929e12 | 0.613724 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047539298e12 | 0.307161 | -0.689167 | 9.38788 |
| user_003 | Sitting | 1.446047539557e12 | 0.498763 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047539824e12 | 0.383802 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047540084e12 | 0.460442 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047540148e12 | 0.422122 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047540261e12 | 0.307161 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047540504e12 | 0.460442 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.446047540803e12 | 0.498763 | -0.765807 | 9.6178 |
| user_003 | Sitting | 1.446047540836e12 | 0.422122 | -0.727487 | 9.2346 |
| user_003 | Sitting | 1.446047540852e12 | 0.537083 | -0.650847 | 9.31124 |
| user_003 | Sitting | 1.446047540884e12 | 0.498763 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047540941e12 | 0.498763 | -0.957409 | 9.54116 |
| user_003 | Sitting | 1.446047541046e12 | 0.345482 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.44604754111e12 | 0.575403 | -0.535886 | 9.50284 |
| user_003 | Sitting | 1.446047541135e12 | 0.460442 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.4460475412e12 | 0.498763 | -0.689167 | 9.27292 |
| user_003 | Sitting | 1.446047541223e12 | 0.307161 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.44604754209e12 | 0.460442 | -0.650847 | 9.46452 |
| user_006 | Sitting | 1.446035991462e12 | -0.229323 | -1.26397 | 9.31124 |
| user_006 | Sitting | 1.44603599224e12 | -0.267643 | -1.30229 | 9.34956 |
| user_006 | Sitting | 1.446035992757e12 | -0.191003 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446035993534e12 | -0.305964 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446035993664e12 | -0.305964 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035994765e12 | -0.229323 | -1.22565 | 9.4262 |
| user_006 | Sitting | 1.446035995413e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.44603599606e12 | -0.229323 | -1.22565 | 9.4262 |
| user_006 | Sitting | 1.446035996578e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035997161e12 | -0.191003 | -1.22565 | 9.4262 |
| user_006 | Sitting | 1.446035998003e12 | -0.267643 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035998391e12 | -0.229323 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446035999492e12 | -0.229323 | -1.18733 | 9.34956 |
| user_006 | Sitting | 1.446036000334e12 | -0.191003 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036000593e12 | -0.229323 | -1.22565 | 9.34956 |
| user_006 | Sitting | 1.446036001046e12 | -0.229323 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446036001176e12 | -0.229323 | -1.18733 | 9.4262 |
| user_006 | Sitting | 1.446036001241e12 | -0.267643 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036001694e12 | -0.152682 | -1.18733 | 9.4262 |
| user_006 | Sitting | 1.44603600273e12 | -0.229323 | -1.26397 | 9.38788 |
| user_006 | Sitting | 1.446036003054e12 | -0.229323 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036003184e12 | -0.191003 | -1.18733 | 9.4262 |
| user_006 | Sitting | 1.446036003378e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036003443e12 | -0.305964 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446036003572e12 | -0.191003 | -1.26397 | 9.38788 |
| user_006 | Sitting | 1.446036003702e12 | -0.305964 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036004091e12 | -0.229323 | -1.22565 | 9.34956 |
| user_006 | Sitting | 1.446036004285e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036004738e12 | -0.229323 | -1.11069 | 9.38788 |
| user_006 | Sitting | 1.446036004997e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036005774e12 | -0.305964 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036006875e12 | -0.152682 | -1.18733 | 9.34956 |
| user_006 | Sitting | 1.446036006939e12 | -0.267643 | -1.30229 | 9.31124 |
| user_006 | Sitting | 1.446036007653e12 | -0.267643 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036007976e12 | -0.229323 | -1.22565 | 9.34956 |
| user_007 | Jogging | 1.446309659174e12 | -12.60678 | -8.85139 | 2.22198 |
| user_007 | Jogging | 1.446309659693e12 | 3.83263 | -1.26397 | 1.83878 |
| user_007 | Jogging | 1.446309661117e12 | -15.90233 | -10.03932 | -2.68302 |
| user_007 | Jogging | 1.446309661377e12 | -7.51018 | -6.74378 | -1.83997 |
| user_007 | Jogging | 1.446309661442e12 | -15.0976 | -12.30022 | 1.45557 |
| user_007 | Jogging | 1.446309661635e12 | -0.344284 | -2.10702 | -0.460442 |
| user_007 | Jogging | 1.446309664809e12 | -3.56319 | -3.98471 | 1.03405 |
| user_007 | Jogging | 1.446309665263e12 | -16.17057 | -11.22725 | -2.91294 |
| user_007 | Jogging | 1.446309665651e12 | -2.98839 | -6.39889 | -0.11556 |
| user_007 | Jogging | 1.446309666687e12 | -3.94639 | 0.613724 | 0.229323 |
| user_007 | Jogging | 1.446309667399e12 | -14.86768 | -3.71647 | -5.82529 |
| user_007 | Jogging | 1.446309671028e12 | -5.13432 | -3.29495 | 0.382604 |
| user_007 | Jogging | 1.446309673683e12 | -7.12698 | -11.64878 | -2.98958 |
| user_007 | Jogging | 1.446309673943e12 | -1.64717 | 0.843646 | -1.87829 |
| user_007 | Jogging | 1.446309674397e12 | -1.60885 | -0.919089 | -1.18853 |
| user_007 | Jogging | 1.446309674979e12 | -13.90968 | -7.81675 | -0.843646 |
| user_007 | Jogging | 1.446309675691e12 | -11.57214 | -12.0703 | -2.49142 |
| user_007 | Jogging | 1.446309675886e12 | -3.79311 | -7.51018 | -1.49509 |
| user_004 | Walking | 1.44604636711e12 | 2.2615 | -7.05034 | -0.728685 |
| user_004 | Walking | 1.446046367952e12 | 3.2195 | -10.23092 | -0.422122 |
| user_004 | Walking | 1.446046368146e12 | -6.82042 | -14.59944 | 0.574206 |
| user_004 | Walking | 1.446046368276e12 | 0.958607 | -6.7821 | 1.03405 |
| user_004 | Walking | 1.44604636957e12 | 3.2195 | -12.4535 | -7.7413 |
| user_004 | Walking | 1.446046369635e12 | 0.843646 | -10.42253 | -7.58802 |
| user_004 | Walking | 1.446046370153e12 | 4.10087 | -10.1926 | -0.307161 |
| user_004 | Walking | 1.446046370347e12 | -7.43354 | -15.97897 | 0.152682 |
| user_004 | Walking | 1.446046371124e12 | 0.230521 | -9.08132 | 7.6042e-2 |
| user_004 | Walking | 1.446046371189e12 | -0.727487 | -8.96635 | 1.14901 |
| user_004 | Walking | 1.446046372095e12 | -1.53221 | -13.18159 | 5.51753 |
| user_004 | Walking | 1.446046373391e12 | -1.68549 | -8.16163 | 1.72382 |
| user_004 | Walking | 1.446046374038e12 | -1.34061 | -1.99206 | -2.95126 |
| user_004 | Walking | 1.446046376822e12 | 0.15388 | -9.54116 | -7.7239e-2 |
| user_004 | Walking | 1.446046377082e12 | 1.41845 | -13.25823 | 0.842448 |
| user_004 | Walking | 1.44604637883e12 | -3.44823 | -2.2603 | 1.22565 |
| user_004 | Walking | 1.446046379219e12 | 2.49142 | -10.72909 | -1.07357 |
| user_004 | Walking | 1.446046379801e12 | 1.80165 | -10.1926 | -7.43474 |
| user_004 | Walking | 1.446046379995e12 | -4.98104 | -6.59049 | 3.21831 |
| user_004 | Walking | 1.446046380349e12 | 0.881966 | -11.34221 | -1.30349 |
| user_004 | Walking | 1.446046380357e12 | -0.229323 | -11.03565 | -1.91661 |
| user_004 | Walking | 1.446046380616e12 | 2.60638 | -8.65979 | -1.18853 |
| user_004 | Walking | 1.446046380632e12 | 1.91661 | -9.04299 | -1.95493 |
| user_004 | Walking | 1.446046380802e12 | -0.152682 | -3.71647 | -4.86728 |
| user_004 | Walking | 1.446046380818e12 | 0.422122 | -5.01936 | -6.47673 |
| user_004 | Walking | 1.446046380826e12 | 0.920286 | -5.67081 | -7.12818 |
| user_004 | Walking | 1.446046380891e12 | 3.18118 | -18.96796 | -9.92556 |
| user_004 | Walking | 1.446046380899e12 | 2.22318 | -16.28553 | -8.58435 |
| user_004 | Walking | 1.446046381053e12 | -3.52487 | -1.76214 | 0.650847 |
| user_004 | Walking | 1.446046381109e12 | -2.6435 | -10.26924 | 2.10702 |
| user_004 | Walking | 1.446046381166e12 | 4.63736 | -9.92436 | -3.29615 |
| user_004 | Walking | 1.446046381377e12 | -1.26397 | -8.96635 | -0.307161 |
| user_004 | Walking | 1.446046381393e12 | -0.497565 | -9.27292 | -1.68669 |
| user_004 | Walking | 1.4460463814e12 | 0.307161 | -9.34956 | -1.38013 |
| user_004 | Walking | 1.446046381539e12 | 0.767005 | -13.10495 | 1.45557 |
| user_004 | Walking | 1.446046381708e12 | 3.41111 | -8.62147 | -2.49142 |
| user_004 | Walking | 1.446046381757e12 | 0.958607 | -7.58682 | -0.307161 |
| user_004 | Walking | 1.446046381773e12 | -0.152682 | -6.51385 | 7.6042e-2 |
| user_004 | Walking | 1.446046381797e12 | -1.72382 | -4.7128 | 1.22565 |
| user_004 | Walking | 1.446046381822e12 | -3.06503 | -2.98839 | 0.727487 |
| user_004 | Walking | 1.446046381854e12 | -3.29495 | -2.03038 | -1.41845 |
| user_004 | Walking | 1.446046381886e12 | -1.14901 | -2.22198 | -4.02423 |
| user_004 | Walking | 1.446046381919e12 | 0.958607 | -6.16897 | -5.40376 |
| user_004 | Walking | 1.446046381959e12 | 3.71767 | -15.05928 | -7.70298 |
| user_004 | Walking | 1.446046382089e12 | -3.71647 | -10.72909 | -2.14654 |
| user_004 | Walking | 1.446046382129e12 | -11.76374 | -19.50444 | -0.843646 |
| user_004 | Walking | 1.44604638221e12 | -4.09967 | -3.40991 | 5.096 |
| user_004 | Walking | 1.446046382259e12 | 0.652044 | -19.58108 | 4.63616 |
| user_004 | Walking | 1.446046382316e12 | -0.229323 | -4.82776 | -0.575403 |
| user_004 | Walking | 1.446046382332e12 | -3.86975 | -4.48288 | 2.14534 |
| user_004 | Walking | 1.446046382526e12 | 1.49509 | -9.77108 | -0.537083 |
| user_004 | Walking | 1.446046382566e12 | 3.2195 | -10.95901 | -7.7239e-2 |
| user_004 | Walking | 1.446046382647e12 | -0.152682 | -13.79471 | 0.574206 |
| user_004 | Walking | 1.44604638285e12 | 2.60638 | -7.77842 | -2.33814 |
| user_004 | Walking | 1.446046383157e12 | -0.344284 | -11.61046 | -6.82161 |
| user_004 | Walking | 1.446046383174e12 | -1.22565 | -8.08499 | -7.08986 |
| user_004 | Jumping | 1.446046440495e12 | -0.612526 | -0.995729 | -0.728685 |
| user_004 | Jumping | 1.446046440559e12 | 1.41845 | -0.880768 | -7.7239e-2 |
| user_004 | Jumping | 1.44604644069e12 | 8.35443 | -7.66346 | -0.460442 |
| user_004 | Jumping | 1.446046441337e12 | 14.02583 | -14.7144 | -0.958607 |
| user_004 | Jumping | 1.446046442243e12 | 0.575403 | 1.15021 | -0.690364 |
| user_004 | Jumping | 1.446046442502e12 | 6.89825 | -11.11229 | -1.57173 |
| user_004 | Jumping | 1.446046443085e12 | 3.94759 | -0.305964 | 1.30229 |
| user_004 | Jumping | 1.446046444705e12 | -1.72382 | 1.26517 | -1.45677 |
| user_004 | Jumping | 1.446046444899e12 | 0.498763 | 1.30349 | 1.18733 |
| user_004 | Jumping | 1.446046445028e12 | 19.19908 | -19.08292 | -3.79431 |
| user_004 | Jumping | 1.446046445093e12 | 3.18118 | -8.46819 | 1.30229 |
| user_004 | Jumping | 1.446046445352e12 | 1.61005 | -0.689167 | -0.613724 |
| user_004 | Jumping | 1.446046445482e12 | 12.03318 | -8.88971 | -0.920286 |
| user_004 | Jumping | 1.446046446387e12 | 1.30349 | -1.57053 | -1.76333 |
| user_004 | Jumping | 1.446046446777e12 | 1.15021 | 6.40009 | -2.41478 |
| user_004 | Jumping | 1.446046446842e12 | -1.41725 | 0.230521 | -1.30349 |
| user_004 | Jumping | 1.446046447036e12 | 0.498763 | 0.690364 | -1.34181 |
| user_004 | Jumping | 1.44604644723e12 | -1.26397 | -3.60151 | 4.40624 |
| user_004 | Jumping | 1.446046448784e12 | 16.36337 | -16.86034 | -1.72501 |
| user_004 | Jumping | 1.446046448849e12 | -0.535886 | -3.83143 | 4.63616 |
| user_004 | Jumping | 1.446046449172e12 | 1.38013 | 2.56806 | 0.191003 |
| user_004 | Jumping | 1.446046449949e12 | -2.0687 | 0.575403 | 1.07237 |
| user_004 | Jumping | 1.446046450079e12 | -1.76214 | -0.459245 | -0.728685 |
| user_004 | Jumping | 1.446046450273e12 | 1.03525 | 0.996927 | -2.33814 |
| user_004 | Jumping | 1.446046450726e12 | 0.805325 | -3.7722e-2 | -0.767005 |
| user_004 | Jumping | 1.446046451115e12 | 0.268841 | 4.40743 | -3.06622 |
| user_004 | Jumping | 1.446046451503e12 | 17.62794 | -14.63776 | -3.41111 |
| user_004 | Jumping | 1.446046451697e12 | -1.80046 | 1.76333 | -2.10822 |
| user_004 | Jumping | 1.446046452086e12 | 3.37279 | -10.34589 | 3.14167 |
| user_004 | Jumping | 1.446046452409e12 | 0.690364 | 1.49509 | -1.68669 |
| user_004 | Jumping | 1.446046453575e12 | -0.305964 | 0.690364 | -1.07357 |
| user_004 | Jumping | 1.446046455194e12 | 0.881966 | 1.87829 | -0.575403 |
Feature Selection on Running Windows
- ETL Using SparkSQL Windows
A Markov Process Assumption
This is sensible since the subjects are not instantaneously changing between the activities of interest: sitting, walking, jogging, etc. Thus it makes sense to try and use the most recent accelerometer readings (from the immediate past) to predict the current activity.
See the following for a crash introduction to windows:
// Import the window functions.
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
// Create a window specification
val windowSize = 10
val wSpec1 = Window.partitionBy("user_id","activity").orderBy("timeStampAsLong").rowsBetween(-windowSize, 0)
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
windowSize: Int = 10
wSpec1: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@ade1233
// Calculate the moving window statistics from data
val dataFeatDF = dataDF
.withColumn( "meanX", mean($"x").over(wSpec1) )
.withColumn( "meanY", mean($"y").over(wSpec1) )
.withColumn( "meanZ", mean($"z").over(wSpec1) )
//resultant = 1/n * ∑ √(x² + y² + z²)
.withColumn( "SqX", pow($"x",2.0) )
.withColumn( "SqY", pow($"y",2.0) )
.withColumn( "SqZ", pow($"z",2.0) )
.withColumn( "resultant", pow( $"SqX"+$"SqY"+$"SqZ",0.50 ) )
.withColumn( "meanResultant", mean("resultant").over(wSpec1) )
// (1 / n ) * ∑ |b - mean_b|, for b in {x,y,z}
.withColumn( "absDevFromMeanX", abs($"x" - $"meanX") )
.withColumn( "absDevFromMeanY", abs($"y" - $"meanY") )
.withColumn( "absDevFromMeanZ", abs($"z" - $"meanZ") )
.withColumn( "meanAbsDevFromMeanX", mean("absDevFromMeanX").over(wSpec1) )
.withColumn( "meanAbsDevFromMeanY", mean("absDevFromMeanY").over(wSpec1) )
.withColumn( "meanAbsDevFromMeanZ", mean("absDevFromMeanZ").over(wSpec1) )
//standard deviation = √ variance = √ 1/n * ∑ (x - u)² with u = mean x
.withColumn( "sqrDevFromMeanX", pow($"absDevFromMeanX",2.0) )
.withColumn( "sqrDevFromMeanY", pow($"absDevFromMeanY",2.0) )
.withColumn( "sqrDevFromMeanZ", pow($"absDevFromMeanZ",2.0) )
.withColumn( "varianceX", mean("sqrDevFromMeanX").over(wSpec1) )
.withColumn( "varianceY", mean("sqrDevFromMeanY").over(wSpec1) )
.withColumn( "varianceZ", mean("sqrDevFromMeanZ").over(wSpec1) )
.withColumn( "stddevX", pow($"varianceX",0.50) )
.withColumn( "stddevY", pow($"varianceY",0.50) )
.withColumn( "stddevZ", pow($"varianceZ",0.50) )
dataFeatDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 27 more fields]
display(dataFeatDF.sample(false,0.1))
| user_id | activity | timeStampAsLong | x | y | z | meanX | meanY | meanZ | SqX | SqY | SqZ | resultant | meanResultant | absDevFromMeanX | absDevFromMeanY | absDevFromMeanZ | meanAbsDevFromMeanX | meanAbsDevFromMeanY | meanAbsDevFromMeanZ | sqrDevFromMeanX | sqrDevFromMeanY | sqrDevFromMeanZ | varianceX | varianceY | varianceZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_001 | Jogging | 1.446047140892e12 | -5.01936 | 0.383802 | -1.61005 | 3.0104863636363643 | -8.294006090909091 | 0.4139589090909091 | 25.193974809599997 | 0.147303975204 | 2.5922610025 | 5.285218991423534 | 12.114445037568451 | 8.029846363636365 | 8.67780809090909 | 2.024008909090909 | 5.574814628099175 | 8.743996363636365 | 1.8780127768595039 | 64.47843262360415 | 75.30435326264728 | 4.0966120640793715 | 39.26105423020038 | 86.44128603240851 | 4.762676135262259 | 6.265864204577081 | 9.297380600599746 | 2.1823556390428807 |
| user_001 | Jogging | 1.446047141346e12 | 0.498763 | -7.51018 | 1.83878 | 2.8084320909090907 | -6.935377181818183 | -4.936863636363633e-2 | 0.24876453016900002 | 56.4028036324 | 3.3811118884000004 | 7.748075893469875 | 10.526229014345958 | 2.309669090909091 | 0.5748028181818174 | 1.8881486363636364 | 4.870479504132231 | 7.062020214876034 | 2.769514024793388 | 5.334571309500826 | 0.3303982797897595 | 3.5651052730018598 | 28.32052218489271 | 67.50085446063852 | 9.63958031560342 | 5.3217029403089295 | 8.215890363231397 | 3.104767352895128 |
| user_001 | Jogging | 1.44604714141e12 | 11.87989 | -19.58108 | 2.75846 | 3.738570272727273 | -7.440509 | -7.027136363636353e-2 | 141.13178641209998 | 383.4186939664 | 7.609101571599999 | 23.06858430745372 | 11.31119913133375 | 8.141319727272727 | 12.140571000000001 | 2.8287313636363636 | 5.357875181818181 | 7.761603768595041 | 2.783764652892562 | 66.28108690168007 | 147.39346420604105 | 8.00172112762004 | 33.64351026346047 | 79.10395343437911 | 9.717968828501292 | 5.800302601025266 | 8.894040332401191 | 3.1173656873233995 |
| user_001 | Jogging | 1.446047142255e12 | 5.32712 | -18.35483 | 3.94639 | 3.254341727272727 | -8.088470909090908 | 0.69265 | 28.378207494399998 | 336.8997843289 | 15.5739940321 | 19.515429430463477 | 14.672311468733064 | 2.0727782727272728 | 10.266359090909091 | 3.25374 | 4.968022991735537 | 9.121183421487604 | 2.997534545454546 | 4.296409767890256 | 105.39812898349174 | 10.5868239876 | 42.373279917383705 | 108.65275154076608 | 11.80757071836034 | 6.509476163055189 | 10.423663057714695 | 3.4362145914305673 |
| user_001 | Jogging | 1.44604714236e12 | -3.90807 | 1.87829 | -1.03525 | -0.38260427272727265 | -4.622226636363637 | 1.309258636363637 | 15.2730111249 | 3.5279733241 | 1.0717425625 | 4.457883692011267 | 6.235267429173871 | 3.5254657272727274 | 6.500516636363637 | 2.3445086363636367 | 2.0246421404958674 | 4.5952694710743796 | 1.275971702479339 | 12.42890859417462 | 42.25671653964041 | 5.496720745983679 | 5.322841770497276 | 27.27538573170004 | 2.7565803183488558 | 2.3071284685724107 | 5.222584200537129 | 1.660295250354242 |
| user_001 | Jogging | 1.446047142611e12 | 3.14286 | -19.58108 | 11.57214 | 4.797603636363636 | -11.600005181818183 | 3.4970007272727273 | 9.877568979600001 | 383.4186939664 | 133.9144241796 | 22.961068945621847 | 17.859074479791374 | 1.6547436363636359 | 7.981074818181817 | 8.075139272727272 | 4.461625685950413 | 7.032252909090909 | 8.588184256198346 | 2.7381765020859485 | 63.69755525341592 | 65.20787427394234 | 29.39875554713526 | 63.52664671148329 | 111.91945306916881 | 5.422061927637424 | 7.970360513269352 | 10.579199075032514 |
| user_001 | Jogging | 1.446047142723e12 | 3.33447 | -16.4005 | 4.59784 | 8.695826363636364 | -19.19787727272727 | 1.5635675454545455 | 11.1186901809 | 268.97640025000004 | 21.140132665599996 | 17.356129265953857 | 21.88260842177062 | 5.361356363636364 | 2.7973772727272674 | 3.0342724545454542 | 5.770848892561983 | 0.974475619834712 | 4.766286942148761 | 28.74414205790414 | 7.825319605971044 | 9.206809328413296 | 47.590515226108074 | 2.4736297184957947 | 29.04037921776094 | 6.898587915371382 | 1.5727777079090977 | 5.3889126192359935 |
| user_001 | Jogging | 1.446047143007e12 | 13.94919 | -19.58108 | -1.99325 | 7.406869090909091 | -15.54699727272727 | -2.132600909090909 | 194.5799016561 | 383.4186939664 | 3.9730455625 | 24.124088401118915 | 17.659646578855703 | 6.5423209090909085 | 4.034082727272731 | 0.13935090909090886 | 4.869846033057851 | 6.8751705371900815 | 2.2795826446280993 | 42.80196287752809 | 16.273823450480194 | 1.9418675864462744e-2 | 25.026920417585174 | 50.28981774904209 | 8.028885422595941 | 5.002691317439561 | 7.091531410706865 | 2.8335287933239606 |
| user_001 | Jogging | 1.446047143412e12 | -3.7722e-2 | -19.38948 | 16.74538 | 2.933844363636364 | -10.001001818181818 | 4.7859561818181815 | 1.422949284e-3 | 375.95193467039996 | 280.4077513444 | 25.61954544803799 | 14.94696596077227 | 2.971566363636364 | 9.388478181818181 | 11.959423818181818 | 4.184833462809917 | 6.708588429752067 | 8.137841272727274 | 8.830206653495043 | 88.14352257047602 | 143.0278180628946 | 25.580893194814248 | 48.75439847000317 | 93.00363187197318 | 5.057755746851981 | 6.9824349957592275 | 9.643839062944444 |
| user_001 | Jogging | 1.446047143428e12 | 5.99e-4 | -19.58108 | 2.49022 | 2.0455097272727274 | -12.96560090909091 | 4.8382107272727275 | 3.5880100000000004e-7 | 383.4186939664 | 6.2011956484 | 19.738791502359028 | 17.17647355875381 | 2.0449107272727276 | 6.615479090909091 | 2.3479907272727276 | 3.7078880247933883 | 7.136446049586777 | 7.307778793388429 | 4.181659882515076 | 43.764563602255365 | 5.513060455358713 | 22.443816935824188 | 54.075963421190316 | 85.24184299019907 | 4.737490573692384 | 7.3536360680407835 | 9.232650918896429 |
| user_001 | Jogging | 1.4460471435e12 | 8.92923 | -19.58108 | 7.62514 | 0.5335997272727272 | -19.581079999999996 | 1.8318090909090905 | 79.73114839290001 | 383.4186939664 | 58.1427600196 | 22.831833092831157 | 21.106302639228264 | 8.395630272727272 | 3.552713678800501e-15 | 5.793330909090909 | 4.901198421487603 | 2.9142437190082675 | 3.942557785123967 | 70.48660767633461 | 1.2621774483536189e-29 | 33.5626830222281 | 33.313236846605356 | 15.557764988992716 | 30.291063062675242 | 5.771762022693361 | 3.944333275598389 | 5.503731739708544 |
| user_001 | Jogging | 1.446047143517e12 | 10.42372 | -19.58108 | 8.73643 | 2.205758909090909 | -19.581079999999996 | 2.4379672727272723 | 108.65393863839999 | 383.4186939664 | 76.3252091449 | 23.841095649103462 | 21.67139468669904 | 8.21796109090909 | 3.552713678800501e-15 | 6.298462727272728 | 6.114780479338842 | 1.5831671074380202 | 4.6259896033057855 | 67.53488449169573 | 1.2621774483536189e-29 | 39.67063272684381 | 45.04989788390212 | 5.722576186684003 | 36.09638973021352 | 6.711922070756045 | 2.3921906668750306 | 6.008027107979251 |
| user_001 | Jogging | 1.446047143581e12 | 14.17911 | -4.75112 | -8.58435 | 12.837903636363638 | -14.822395454545456 | 1.539181818181818 | 201.04716039209998 | 22.573141254400003 | 73.6910649225 | 17.24271923360698 | 21.99929601707681 | 1.3412063636363616 | 10.071275454545455 | 10.123531818181819 | 6.092612942148761 | 3.6242771074380187 | 7.671663884297521 | 1.7988345098586722 | 101.43058928132977 | 102.48589647373969 | 41.440687896949285 | 28.9054852091499 | 72.55417043068063 | 6.437444205346504 | 5.376382167326827 | 8.517873586211563 |
| user_001 | Jogging | 1.446047143606e12 | 9.23579 | -0.497565 | 0.919089 | 13.25594272727273 | -10.042807727272727 | -1.2721355454545458 | 85.2998169241 | 0.24757092922499999 | 0.8447245899210001 | 9.294735738214724 | 18.876966595567662 | 4.02015272727273 | 9.545242727272727 | 2.1912245454545456 | 4.424255107438015 | 6.262994958677688 | 6.90810826446281 | 16.161627950598373 | 91.11165872255289 | 4.80146500860248 | 24.680606942273855 | 54.43818419281497 | 66.47172910922194 | 4.967958025413847 | 7.378223647519434 | 8.153019631352665 |
| user_001 | Jogging | 1.44604714363e12 | 5.82529 | 3.14286 | 3.21831 | 11.935633636363635 | -4.315663545454545 | -2.742242818181819 | 33.9340035841 | 9.877568979600001 | 10.357519256099998 | 7.359965476807618 | 14.48403310791005 | 6.110343636363635 | 7.458523545454545 | 5.960552818181819 | 4.2234690247933875 | 8.366494983471073 | 7.1579815123966934 | 37.33629935444957 | 55.629573478099836 | 35.52818989833523 | 22.091004368014264 | 72.82379547301899 | 68.08732499721447 | 4.700106846446607 | 8.53368592537943 | 8.251504408119436 |
| user_001 | Jogging | 1.446047143687e12 | 0.805325 | 5.40376 | -1.18853 | 4.815022272727273 | 3.9789428181818183 | 1.4346706363636363 | 0.6485483556249999 | 29.2006221376 | 1.4126035609000003 | 5.591222947989554 | 7.279610887559767 | 4.009697272727273 | 1.4248171818181818 | 2.623200636363636 | 4.808407685950414 | 5.901961702479338 | 3.262293380165289 | 16.07767221891653 | 2.0301040016043057 | 6.881181578618586 | 23.86087788087626 | 40.88601350062889 | 14.184341683283828 | 4.8847597567205145 | 6.394217192168944 | 3.7662105203086864 |
| user_001 | Jogging | 1.446047143703e12 | 0.498763 | 3.67935 | -5.0972 | 3.3832365454545457 | 4.700061818181818 | 0.36518618181818213 | 0.24876453016900002 | 13.5376164225 | 25.98144784 | 6.306173863181145 | 6.765815236942559 | 2.884473545454546 | 1.0207118181818182 | 5.462386181818182 | 4.530347388429752 | 4.320377892561984 | 3.515650859504132 | 8.320187634427118 | 1.041852615776033 | 29.83766279931822 | 21.64134645781683 | 25.2878319216034 | 16.07774203478182 | 4.652026059451606 | 5.028700818462299 | 4.009705978595166 |
| user_001 | Jogging | 1.4460471438e12 | 6.36177 | -19.58108 | 6.55217 | 2.181372181818182 | -11.164546363636363 | 3.423636363636378e-2 | 40.4721175329 | 383.4186939664 | 42.930931708900005 | 21.60605802103197 | 12.990120579315441 | 4.180397818181818 | 8.416533636363637 | 6.517933636363637 | 2.872756842975207 | 9.207959504132232 | 3.702816834710744 | 17.475725918259307 | 70.83803845204051 | 42.4834588880405 | 10.08973643029246 | 92.24096030173884 | 21.23336541278622 | 3.1764345468295834 | 9.604215756725733 | 4.607967601099884 |
| user_001 | Jogging | 1.446047143865e12 | 7.66466 | -19.58108 | 2.37526 | 6.337383636363636 | -19.581079999999996 | 3.5875754545454543 | 58.747012915599996 | 383.4186939664 | 5.6418600676 | 21.1614641967327 | 21.061833583938864 | 1.3272763636363631 | 3.552713678800501e-15 | 1.2123154545454544 | 2.492719975206612 | 4.63169049586777 | 2.403411570247934 | 1.7616625454677672 | 1.2621774483536189e-29 | 1.4697087613297517 | 7.974185257215893 | 38.68784266457513 | 9.625501871091812 | 2.82385999249536 | 6.21995519795562 | 3.1024992942935237 |
| user_001 | Jogging | 1.446047143906e12 | 2.22318 | -17.58843 | 3.67815 | 5.58143 | -19.372060909090905 | 3.406424545454546 | 4.9425293124000005 | 309.35286986489996 | 13.5287874225 | 18.10591579014439 | 20.5777606506816 | 3.35825 | 1.7836309090909062 | 0.27172545454545416 | 1.7852188099173552 | 0.911135950413226 | 1.229100991735537 | 11.2778430625 | 3.1813392198644523 | 7.383472264793367e-2 | 4.562496695217112 | 2.185291967910373 | 2.2448866682423736 | 2.136000162738082 | 1.4782733062293905 | 1.4982945866025057 |
| user_001 | Jogging | 1.446047143914e12 | 2.03158 | -15.25089 | 2.91174 | 5.28531909090909 | -18.97840727272727 | 3.3541690909090907 | 4.127317296399999 | 232.58964579210001 | 8.4782298276 | 15.658709810073754 | 20.13034914244241 | 3.2537390909090904 | 3.7275172727272707 | 0.44242909090909066 | 1.9736531404958675 | 0.9219033057851264 | 1.1711456198347105 | 10.586818071709914 | 13.89438501848015 | 0.1957435004826444 | 5.398146523816002 | 2.2642876788123227 | 2.1566573067818178 | 2.32339116892012 | 1.5047550228566517 | 1.468556198033231 |
| user_001 | Jogging | 1.446047143946e12 | 0.805325 | -4.67448 | 1.80046 | 3.285695 | -15.020963636363634 | 2.7131754545454543 | 0.6485483556249999 | 21.850763270399998 | 3.2416562115999996 | 5.073555739087233 | 15.738688413790378 | 2.48037 | 10.346483636363633 | 0.9127154545454543 | 2.33848826446281 | 3.4744802479338843 | 0.8097950413223138 | 6.152235336900001 | 107.04972363754042 | 0.8330495009661153 | 7.003361392995342 | 26.536731773096072 | 0.9551943565163034 | 2.6463864783880946 | 5.15138154023715 | 0.9773404506702377 |
| user_001 | Jogging | 1.446047144043e12 | -4.78944 | 6.93658 | 0.114362 | -4.071804181818182 | 5.051913545454545 | -2.2614949999999996 | 22.938735513599998 | 48.116142096400004 | 1.3078667044000002e-2 | 8.430181271897064 | 7.023773744750437 | 0.7176358181818179 | 1.8846664545454548 | 2.3758569999999994 | 2.8974588016528924 | 7.285610578512396 | 2.8448858760330578 | 0.5150011675374871 | 3.5519676448889346 | 5.644696484448997 | 9.729573142987029 | 65.17831055224397 | 9.956481900398632 | 3.119226369308106 | 8.073308525768352 | 3.1553893421254107 |
| user_001 | Jogging | 1.446047144092e12 | -1.99206 | 4.21583 | -2.98958 | -4.1693490909090904 | 6.340868181818181 | -2.1778882727272726 | 3.9683030435999997 | 17.773222588900005 | 8.937588576400001 | 5.538873008916164 | 8.085371578268587 | 2.1772890909090905 | 2.125038181818181 | 0.8116917272727275 | 1.5590985867768594 | 2.253931280991736 | 1.5172942727272727 | 4.7405877853917335 | 4.51578727418512 | 0.6588434601229839 | 3.3676530779136438 | 8.457330240795045 | 3.1033527599789106 | 1.8351166387763052 | 2.9081489371755094 | 1.761633548720877 |
| user_001 | Jogging | 1.446047144115e12 | 3.60271 | 3.8919e-2 | -6.36177 | -2.4414504545454547 | 4.870761454545455 | -2.3311700909090907 | 12.9795193441 | 1.514688561e-3 | 40.4721175329 | 7.3111662247250955 | 6.858484763711422 | 6.044160454545455 | 4.8318424545454555 | 4.03059990909091 | 1.9356506115702479 | 2.061379578512397 | 1.4881586776859501 | 36.53187560029112 | 23.346701505547852 | 16.24573562716365 | 6.431645076172794 | 6.610973877450948 | 3.3286675864357367 | 2.536068823232681 | 2.5711814166742393 | 1.824463643495188 |
| user_001 | Jogging | 1.44604714414e12 | 6.28513 | -1.72382 | 3.60151 | 0.6938477272727271 | 3.0348691818181806 | -2.150019636363636 | 39.5028591169 | 2.9715553923999996 | 12.970874280100002 | 7.446159331454035 | 6.6932802788723516 | 5.591282272727272 | 4.7586891818181805 | 5.751529636363636 | 3.6945847933884295 | 2.4996878512396696 | 2.011975041322314 | 31.262437453314252 | 22.645122729153385 | 33.08009315796922 | 20.548499768802852 | 9.118378197963445 | 7.077055657840811 | 4.533045308487756 | 3.0196652460104656 | 2.6602736058234333 |
| user_001 | Jogging | 1.44604714418e12 | -3.56319 | -9.92436 | -3.98591 | 2.9617137272727274 | -2.009475363636364 | 0.6369127272727273 | 12.696322976100001 | 98.4929214096 | 15.8874785281 | 11.272831184480676 | 7.331966995972979 | 6.5249037272727275 | 7.914884636363636 | 4.622822727272728 | 4.575635462809917 | 4.63612326446281 | 4.079687826446281 | 42.57436865017753 | 62.64539880694513 | 21.370489967789258 | 26.438045152664895 | 23.685324490452658 | 22.525681280821853 | 5.141793962486721 | 4.866757081512561 | 4.74612276293206 |
| user_001 | Jogging | 1.446047144221e12 | 4.714 | -19.58108 | 7.6042e-2 | 3.884886 | -8.196463636363637 | 3.967295636363637 | 22.221796000000005 | 383.4186939664 | 5.782385764e-3 | 20.140662162703688 | 14.018026468325372 | 0.8291140000000006 | 11.384616363636363 | 3.8912536363636367 | 3.830131181818182 | 6.218974305785125 | 8.42825211570248 | 0.6874300249960009 | 129.60948974717684 | 15.141854862513226 | 19.765861701657393 | 44.709337568159334 | 94.92463205415766 | 4.445881431353899 | 6.686504136554418 | 9.742927283632865 |
| user_001 | Jogging | 1.446047144254e12 | -1.18733 | -19.58108 | 7.93171 | 2.818885454545455 | -14.29636090909091 | 4.907885636363637 | 1.4097525289 | 383.4186939664 | 62.9120235241 | 21.159878780829533 | 19.09860199176969 | 4.006215454545455 | 5.284719090909091 | 3.023824363636363 | 3.438377512396694 | 7.292894024793389 | 7.597556247933884 | 16.049762268238844 | 27.928255869819008 | 9.143513782120856 | 17.21997488706634 | 58.23402061541601 | 88.07572005384371 | 4.1496957583739 | 7.6311218451428235 | 9.38486654427455 |
| user_001 | Jogging | 1.446047144278e12 | 1.22685 | -19.58108 | 5.44089 | 3.083644 | -17.351535454545452 | 8.287040181818181 | 1.5051609225 | 383.4186939664 | 29.603283992099996 | 20.35993955985626 | 20.824887979127013 | 1.856794 | 2.229544545454548 | 2.8461501818181816 | 3.1305487933884297 | 6.410893917355373 | 5.5722790413223136 | 3.4476839584360004 | 4.970868880166127 | 8.100570857463667 | 14.594451388452198 | 49.33036109995855 | 50.57366699564773 | 3.8202684969059697 | 7.023557581451052 | 7.111516504631606 |
| user_001 | Jogging | 1.446047144311e12 | 11.49669 | -19.58108 | 7.39522 | 4.355181272727273 | -19.535792727272725 | 6.182906363636363 | 132.1738809561 | 383.4186939664 | 54.6892788484 | 23.880574820780595 | 21.77720408013568 | 7.1415087272727265 | 4.528727272727551e-2 | 1.2123136363636373 | 4.527814231404959 | 3.726571074380167 | 2.767612314049586 | 51.00114690171252 | 2.050937071074632e-3 | 1.4697043529132254 | 29.305284421350223 | 23.940853207647194 | 8.62882105866129 | 5.413435546984024 | 4.892939117508739 | 2.937485499310812 |
| user_001 | Jogging | 1.446047144521e12 | 2.95126 | -1.11069 | -7.47306 | 2.6133480000000002 | 2.1569868181818177 | -3.048805 | 8.7099355876 | 1.2336322760999998 | 55.8466257636 | 8.111115436689333 | 5.523819861858547 | 0.33791199999999977 | 3.2676768181818177 | 4.4242550000000005 | 3.396256438016529 | 3.6597486033057858 | 3.397523859504133 | 0.11418451974399985 | 10.677711788082847 | 19.574032305025003 | 16.3348600923496 | 20.826787642321772 | 14.775003279205595 | 4.041640767355457 | 4.5636375450206135 | 3.8438266453113616 |
| user_001 | Jogging | 1.44604714465e12 | 1.99325 | -19.58108 | 4.25296 | 3.5539354545454547 | -19.33373999999999 | 3.173021363636363 | 3.9730455625 | 383.4186939664 | 18.0876687616 | 20.136519269488954 | 20.04234972512039 | 1.5606854545454547 | 0.24734000000000833 | 1.0799386363636367 | 1.341211223140496 | 5.445601107438019 | 1.5103279338842974 | 2.4357390880297527 | 6.117707560000412e-2 | 1.1662674583109511 | 2.0902997020591165 | 46.50264074581899 | 3.8864219929065356 | 1.4457868798889817 | 6.819284474621878 | 1.9714010228531726 |
| user_001 | Jogging | 1.446047144779e12 | -0.191003 | 0.920286 | -0.230521 | 2.95823 | -5.57326509090909 | 0.24325772727272724 | 3.6482146009e-2 | 0.8469263217960001 | 5.3139931441e-2 | 0.9677543072732873 | 6.782599679498083 | 3.1492329999999997 | 6.49355109090909 | 0.47377872727272724 | 1.9058816776859502 | 7.382202495867769 | 1.6845096694214876 | 9.917668488288998 | 42.166205770246634 | 0.22446628241616526 | 4.348254975048362 | 56.287978164098156 | 4.396775017887605 | 2.0852469817861774 | 7.502531450390472 | 2.096848830480539 |
| user_001 | Jogging | 1.446047144909e12 | 3.44943 | 2.8363 | -4.59904 | -1.8422599999999998 | 5.741678181818181 | -3.3414341818181814 | 11.8985673249 | 8.04459769 | 21.151168921599997 | 6.410486248054823 | 8.646625001195995 | 5.29169 | 2.905378181818181 | 1.2576058181818182 | 1.700345950413223 | 2.538641636363636 | 4.040733280991735 | 28.0019830561 | 8.44122237938512 | 1.5815723939247603 | 5.881941722491503 | 7.297593555367826 | 31.585934417060354 | 2.4252714739780172 | 2.7014058479554355 | 5.620136512315368 |
| user_001 | Jogging | 1.446047144965e12 | -1.49389 | -10.80573 | -8.50771 | 1.8608763636363634 | -1.807423636363636 | -0.2583900000000001 | 2.2317073320999996 | 116.76380083290002 | 72.3811294441 | 13.833894520672768 | 10.624124936163746 | 3.3547663636363634 | 8.998306363636365 | 8.249319999999999 | 3.971061611570248 | 5.76831652892562 | 7.454093057851239 | 11.254457354585949 | 80.9695174138587 | 68.05128046239999 | 21.70051268411826 | 45.90407271894876 | 64.16546233071315 | 4.658380908010664 | 6.775254439425043 | 8.010334720266885 |
| user_001 | Jogging | 1.446047145006e12 | 5.2888 | -19.50444 | 1.26397 | 4.592069272727273 | -9.882558181818181 | 4.744153636363636 | 27.97140544 | 380.42317971359995 | 1.5976201609 | 20.248264254362642 | 15.73332539025025 | 0.696730727272727 | 9.621881818181818 | 3.4801836363636363 | 3.560938619834711 | 8.04219826446281 | 9.370108743801651 | 0.4854337063259831 | 92.58060972305783 | 12.111678142813222 | 18.604081060610227 | 68.5146688204746 | 105.59536052408288 | 4.3132448412546935 | 8.277358807039514 | 10.275960321258685 |
| user_001 | Jogging | 1.446047145063e12 | 3.83263 | -19.58108 | 6.51385 | 2.3694895454545457 | -17.786991818181818 | 6.966730909090909 | 14.6890527169 | 383.4186939664 | 42.430241822499994 | 20.988996843722663 | 20.711596889168277 | 1.4631404545454543 | 1.7940881818181822 | 0.4528809090909096 | 3.6613319669421487 | 5.671721735537191 | 3.724989256198347 | 2.1407799897274784 | 3.218752404139671 | 0.20510111781900875 | 18.391050523378283 | 38.52759670800473 | 38.85600186927499 | 4.288478812280443 | 6.207060230737634 | 6.233458259206922 |
| user_001 | Jogging | 1.446047145095e12 | 8.39275 | -19.58108 | 7.77842 | 3.1184786363636365 | -19.581079999999996 | 5.907696363636364 | 70.4382525625 | 383.4186939664 | 60.50381769639999 | 22.679523015824206 | 21.447303073035563 | 5.2742713636363625 | 3.552713678800501e-15 | 1.8707236363636355 | 5.020279380165289 | 3.0450398347107455 | 1.37161479338843 | 27.817938417274576 | 1.2621774483536189e-29 | 3.4996069236495835 | 31.002898462194388 | 16.330388050525777 | 2.60981791889955 | 5.568024646335035 | 4.041087483651619 | 1.6154930884716128 |
| user_001 | Jogging | 1.446047145298e12 | 2.4531 | 2.68302 | -2.18486 | 2.9512618181818184 | 2.784047636363636 | -1.7006264545454548 | 6.01769961 | 7.1985963204 | 4.7736132196000005 | 4.241451302325656 | 4.838002482542072 | 0.49816181818181837 | 0.10102763636363621 | 0.4842335454545452 | 2.036044016528926 | 4.023950479338843 | 1.7253638842975207 | 0.24816519709421506 | 1.0206583309223109e-2 | 0.2344821265434791 | 5.415028704769436 | 23.42669537789521 | 4.255417333194267 | 2.3270214233585036 | 4.84011315755068 | 2.062866290672827 |
| user_001 | Jogging | 1.446047145826e12 | 3.18118 | -19.58108 | 4.7128 | 5.462984545454545 | -11.24467181818182 | 3.880204545454545 | 10.1199061924 | 383.4186939664 | 22.21048384 | 20.389926042013983 | 16.88118003999633 | 2.2818045454545453 | 8.33640818181818 | 0.8325954545454546 | 3.494114983471074 | 8.170460165289256 | 7.975376504132232 | 5.206631983657024 | 69.4957013738851 | 0.6932151909297521 | 16.238127510167725 | 74.18152678299337 | 85.41361229590531 | 4.0296560039496825 | 8.612869834323131 | 9.241948511861843 |
| user_001 | Jogging | 1.446047145906e12 | 7.24314 | -19.58108 | 1.57053 | 1.3661963636363637 | -19.581079999999996 | 4.9705900000000005 | 52.4630770596 | 383.4186939664 | 2.4665644809 | 20.936769939675507 | 20.824778135048515 | 5.876943636363636 | 3.552713678800501e-15 | 3.4000600000000007 | 4.809673801652893 | 3.080192148760331 | 2.9028430578512396 | 34.53846650499504 | 1.2621774483536189e-29 | 11.560408003600005 | 26.078049228796736 | 17.058755092359807 | 9.280744192829529 | 5.10666713510845 | 4.130224581346614 | 3.0464313865290857 |
| user_001 | Jogging | 1.446047145914e12 | 4.79064 | -19.58108 | 2.52854 | 1.5125109090909092 | -19.581079999999996 | 4.772020909090909 | 22.9502316096 | 383.4186939664 | 6.3935145316 | 20.316555813119503 | 20.818108114239923 | 3.2781290909090908 | 3.552713678800501e-15 | 2.2434809090909087 | 4.9002487603305775 | 2.3223368595041345 | 3.0311053719008267 | 10.746130336664462 | 1.2621774483536189e-29 | 5.03320658945537 | 26.581639988161047 | 10.74096405837025 | 9.675288865422766 | 5.155738549244041 | 3.277341004285372 | 3.1105126370781337 |
| user_001 | Jogging | 1.446047145947e12 | 2.68302 | -16.24721 | 0.267643 | 3.2264732727272727 | -19.002790909090912 | 2.504155 | 7.1985963204 | 263.97183278409995 | 7.163277544900001e-2 | 16.46942809814442 | 19.77419717829964 | 0.5434532727272727 | 2.755580909090913 | 2.236512 | 2.9690325619834708 | 0.9991787603305805 | 2.7372102479338847 | 0.29534145963798347 | 7.593226146546302 | 5.001985926143999 | 13.834117142120611 | 2.145853908953498 | 7.944784113633844 | 3.719424302512502 | 1.4648733422905538 | 2.8186493420845813 |
| user_001 | Jogging | 1.446047146142e12 | 2.49142 | -2.22198 | -3.41111 | 6.225908181818181 | 3.0348706363636366 | -3.9092709090909086 | 6.207173616400001 | 4.937195120399999 | 11.635671432099999 | 4.772844033582074 | 8.453049060126027 | 3.734488181818181 | 5.2568506363636365 | 0.4981609090909087 | 2.676405371900827 | 3.922289024793389 | 1.6018522727272726 | 13.946401980139665 | 27.63447861303677 | 0.24816429134628062 | 8.224617051718033 | 20.548089963168977 | 5.414473050834811 | 2.867859315189299 | 4.533000106239683 | 2.3269020286283673 |
| user_001 | Jogging | 1.446047146191e12 | 2.2615 | -18.23987 | 1.8771 | 3.0209363636363644 | -5.601133909090908 | -1.3069736363636362 | 5.114382249999999 | 332.69285761689997 | 3.52350441 | 18.475138545540055 | 8.593185614974145 | 0.7594363636363646 | 12.638736090909092 | 3.184073636363636 | 2.3581242148760326 | 6.585709553719008 | 2.062647512396694 | 0.5767435904132245 | 159.73764997564803 | 10.13832492178595 | 6.783338460343121 | 54.960802016525165 | 6.4349872719740056 | 2.6044842983483547 | 7.413555288559273 | 2.5367276700454084 |
| user_001 | Jogging | 1.446047146222e12 | 6.70665 | -19.58108 | 1.34061 | 3.292661818181818 | -13.000438181818181 | 0.8041272727272727 | 44.9791542225 | 383.4186939664 | 1.7972351721000002 | 20.741144697460648 | 13.747680096855284 | 3.4139881818181816 | 6.580641818181819 | 0.5364827272727274 | 1.9527515702479334 | 8.756033107438014 | 2.4198804049586773 | 11.655315305594213 | 43.30484673920332 | 0.2878137166619836 | 5.276590136867767 | 83.31892515202385 | 7.355384939688656 | 2.2970829625565914 | 9.127920089046784 | 2.7120812929719964 |
| user_001 | Jogging | 1.446047146247e12 | 6.70665 | -19.58108 | -2.0699 | 4.581617272727272 | -17.348051818181816 | 1.1106894545454544 | 44.9791542225 | 383.4186939664 | 4.28448601 | 20.801017624118778 | 18.08485443258767 | 2.1250327272727274 | 2.2330281818181845 | 3.1805894545454545 | 1.8029535537190085 | 8.180911776859503 | 2.438882479338843 | 4.515764091980166 | 4.986414860794227 | 10.116149278365752 | 4.334497354961382 | 78.27161570846312 | 7.517023131813722 | 2.081945569644265 | 8.847124714191787 | 2.7417190103680795 |
| user_001 | Jogging | 1.446047146255e12 | 6.55337 | -19.58108 | -1.61005 | 4.947401818181818 | -18.389667272727266 | 0.8738004545454544 | 42.9466583569 | 383.4186939664 | 2.5922610025 | 20.71129192797494 | 19.18895665104643 | 1.6059681818181826 | 1.1914127272727342 | 2.4838504545454545 | 1.8089707438016531 | 7.583304611570248 | 2.3552742644628095 | 2.579133801012399 | 1.4194642867074545 | 6.169513080545661 | 4.353425914253344 | 72.91914457913747 | 7.02478885722426 | 2.086486499897218 | 8.539270728764691 | 2.6504318246701346 |
| user_001 | Jogging | 1.446047146271e12 | 5.74865 | -19.58108 | -0.307161 | 5.564010909090908 | -19.452184545454543 | 0.41047318181818165 | 33.04697682249999 | 383.4186939664 | 9.434787992100001e-2 | 20.409802024243668 | 20.332815201913306 | 0.18463909090909159 | 0.12889545454545726 | 0.7176341818181817 | 1.7611500826446287 | 5.651453752066117 | 1.715229809917355 | 3.4091593891735786e-2 | 1.6614038202480037e-2 | 0.5149988189138511 | 4.313352366296018 | 51.02793158470397 | 3.854774185943344 | 2.076861181277174 | 7.143383762944839 | 1.9633578853442244 |
| user_001 | Jogging | 1.446047146295e12 | 4.10087 | -19.58108 | -0.422122 | 5.940246363636363 | -19.581079999999996 | -7.566363636363579e-3 | 16.817134756899996 | 383.4186939664 | 0.178186982884 | 20.010347715774056 | 20.51464954273569 | 1.8393763636363634 | 3.552713678800501e-15 | 0.4145556363636364 | 1.8916290082644627 | 2.4962036611570273 | 1.1429585702479337 | 3.3833054071041313 | 1.2621774483536189e-29 | 0.17185637564085954 | 4.597386198170472 | 14.204368818450723 | 2.222369381335169 | 2.1441516266743994 | 3.7688683737231687 | 1.490761342849743 |
| user_001 | Jogging | 1.44604714649e12 | 0.958607 | 3.52607 | 0.344284 | -0.678715818181818 | 1.8713269090909093 | 2.333455818181818 | 0.918927380449 | 12.4331696449 | 0.11853147265599999 | 3.6702354826366386 | 3.7110363855085042 | 1.637322818181818 | 1.6547430909090906 | 1.989171818181818 | 1.7076289090909091 | 3.528951776859505 | 1.723146338842975 | 2.680826010938851 | 2.7381746969113707 | 3.95680452224876 | 3.4146557666225394 | 14.091008592012436 | 3.72855255493712 | 1.847878720755921 | 3.753799221057572 | 1.9309460258995124 |
| user_001 | Jogging | 1.446047146627e12 | 8.77595 | -15.13592 | 11.64878 | 3.8744349090909096 | -4.806858181818182 | -2.1465371818181818 | 77.0172984025 | 229.0960742464 | 135.69407548840002 | 21.01921616372266 | 10.213391746577853 | 4.90151509090909 | 10.329061818181819 | 13.795317181818183 | 3.0817764958677687 | 5.029777553719009 | 5.655888396694215 | 24.024850186409545 | 106.6895180438215 | 190.31077614696795 | 12.519304124182344 | 31.099654858346547 | 56.03006032522963 | 3.538262868157529 | 5.576706452588889 | 7.485322994048395 |
| user_001 | Jogging | 1.4460471467e12 | 0.881966 | -19.58108 | -1.45677 | 3.3065973636363637 | -18.302574545454544 | 6.210776818181818 | 0.7778640251560001 | 383.4186939664 | 2.1221788328999995 | 19.654992669152943 | 20.497246490507834 | 2.4246313636363634 | 1.2785054545454564 | 7.667546818181818 | 3.0073537190082646 | 6.956878148760333 | 7.472461669421488 | 5.8788372495291314 | 1.6345761973024842 | 58.79127420901012 | 11.837209034079082 | 60.051072653539926 | 68.10106476024005 | 3.440524528916933 | 7.7492627167711845 | 8.252336927212804 |
| user_001 | Jogging | 1.446047146708e12 | 2.18486 | -19.58108 | -0.1922 | 2.616831909090909 | -19.065497272727267 | 5.611586818181818 | 4.7736132196000005 | 383.4186939664 | 3.694084e-2 | 19.703533896892708 | 20.818008869068755 | 0.4319719090909091 | 0.5155827272727329 | 5.803786818181818 | 2.473402603305785 | 6.281047404958678 | 7.099076429752066 | 0.18659973024364462 | 0.26582554866198926 | 33.68394143090103 | 8.239763534897493 | 54.32996020046944 | 62.23338618681305 | 2.8704988303250523 | 7.370885984769364 | 7.888813991140433 |
| user_001 | Jogging | 1.446047146789e12 | 5.94025 | -12.68342 | 0.804128 | 5.170359090909091 | -17.612809090909092 | 2.316038 | 35.2865700625 | 160.8691428964 | 0.646621840384 | 14.02862554918635 | 18.616420879792923 | 0.7698909090909085 | 4.929389090909092 | 1.5119099999999999 | 2.1500545950413223 | 1.5967857024793408 | 1.880862933884297 | 0.5927320119008256 | 24.298876809573567 | 2.2858718480999998 | 5.820509118572988 | 5.918452160384152 | 5.775440422698065 | 2.4125731322745407 | 2.4327869122436825 | 2.4032146018818348 |
| user_001 | Jogging | 1.446047146846e12 | 14.94552 | 2.52974 | -7.08986 | 8.124505454545455 | -8.865327727272726 | -0.9760245454545455 | 223.36856807040002 | 6.3995844675999995 | 50.2661148196 | 16.734224432509563 | 14.08539729512732 | 6.821014545454545 | 11.395067727272727 | 6.1138354545454545 | 2.543391082644628 | 6.365605413223141 | 2.754627909090909 | 46.52623942930248 | 129.84756850913243 | 37.37898396525703 | 11.835538075822928 | 47.25518283891866 | 14.352842783440092 | 3.4402816855343294 | 6.874240528154267 | 3.7885145879935704 |
| user_001 | Jogging | 1.44604714691e12 | 2.6447 | 6.51505 | -2.14654 | 8.817753636363635 | 3.034871363636364 | -3.5852909090909093 | 6.994438089999999 | 42.44587650249999 | 4.607633971599999 | 7.351730991004771 | 10.905216461572586 | 6.173053636363635 | 3.4801786363636356 | 1.4387509090909094 | 4.208898595041322 | 7.25900756198347 | 2.4344469173553724 | 38.1065911974223 | 12.111643341001853 | 2.0700041784099183 | 22.049739906490682 | 58.57205085462563 | 13.497022673672406 | 4.69571505805992 | 7.653237932707021 | 3.673829429038916 |
| user_001 | Jogging | 1.446047146926e12 | 0.881966 | 6.09353 | -1.80165 | 6.790261454545456 | 4.644324545454546 | -2.839787272727272 | 0.7778640251560001 | 37.1311078609 | 3.2459427224999997 | 6.4152096309127735 | 9.492024266035791 | 5.908295454545455 | 1.4492054545454547 | 1.0381372727272722 | 4.2548196694214875 | 5.969101487603305 | 1.4526874958677687 | 34.90795517820249 | 2.1001964494842977 | 1.0777289970256188 | 22.276377763682646 | 44.06520485639808 | 4.615243289794645 | 4.719785775189658 | 6.63816276212011 | 2.1483117301254593 |
| user_001 | Jogging | 1.446047146991e12 | 5.71033 | -7.7401 | 3.67815 | 1.2895554545454546 | 1.2895539999999999 | 0.4069892727272728 | 32.6078687089 | 59.90914801 | 13.5287874225 | 10.29785434648403 | 5.452298577041354 | 4.420774545454545 | 9.029654 | 3.2711607272727274 | 4.018882578512398 | 4.053401785123968 | 2.1415043057851237 | 19.543247581738843 | 81.53465135971601 | 10.70049250365144 | 19.656272774198364 | 24.692266006893522 | 5.525528248539532 | 4.433539531141948 | 4.969131313106298 | 2.3506442198979265 |
| user_001 | Jogging | 1.446047147008e12 | 5.86361 | -14.5228 | 1.64717 | 1.996739090909091 | -2.2498487272727274 | 1.2430692727272727 | 34.3819222321 | 210.91171984000002 | 2.7131690089 | 15.748231998576857 | 6.790361057403035 | 3.8668709090909097 | 12.272951272727273 | 0.4041007272727273 | 3.7151708429752066 | 5.61915094214876 | 2.1373874958677685 | 14.952690627573558 | 150.62533294273797 | 0.16329739778234711 | 16.52413057886283 | 47.64363520050973 | 5.706252704011319 | 4.064988386067398 | 6.902436903044441 | 2.3887764031008256 |
| user_001 | Jogging | 1.44604714755e12 | 16.86154 | -19.58108 | -1.49509 | 6.253777272727273 | -19.581079999999996 | 3.8035627272727273 | 284.31153117160005 | 383.4186939664 | 2.2352941081 | 25.883692148650276 | 21.360767742613746 | 10.607762727272728 | 3.552713678800501e-15 | 5.298652727272727 | 3.1489165289256196 | 3.1394150413223163 | 2.8486857355371904 | 112.52463007811656 | 1.2621774483536189e-29 | 28.07572072423471 | 16.449431349785637 | 18.39730278650308 | 10.253797064157284 | 4.055789855229883 | 4.289207710813628 | 3.2021550656014903 |
| user_001 | Jogging | 1.446047147622e12 | 2.37646 | -6.36057 | -5.13552 | 6.630013181818182 | -14.672596363636364 | 0.23280781818181825 | 5.647562131599999 | 40.4568507249 | 26.373565670399998 | 8.513399939325064 | 17.26086851800695 | 4.253553181818182 | 8.312026363636363 | 5.368327818181818 | 5.596982917355372 | 3.5789906611570257 | 3.841849239669422 | 18.09271467055558 | 69.08978226978594 | 28.81894356346476 | 38.34505562899607 | 23.791265035335616 | 19.437297262372166 | 6.192338462083292 | 4.877629038306995 | 4.408775029684795 |
| user_001 | Jogging | 1.446047147639e12 | 2.10822 | -3.86975 | -4.36911 | 4.651291363636364 | -11.934435454545456 | -0.6485594545454547 | 4.444591568400001 | 14.974965062499999 | 19.0891221921 | 6.205536143074182 | 14.146659379883403 | 2.543071363636364 | 8.064685454545456 | 3.7205505454545453 | 4.807773413223141 | 5.056698264462809 | 3.875102727272727 | 6.467211960547316 | 65.03915148075706 | 13.842496361282114 | 28.276395911400765 | 35.80188864520639 | 19.261341575418438 | 5.317555445070673 | 5.983467944696152 | 4.388774495849432 |
| user_001 | Jogging | 1.446047147655e12 | 3.2195 | -0.995729 | -4.86728 | 2.839785 | -8.691143545454544 | -1.735463090909091 | 10.36518025 | 0.991476241441 | 23.6904145984 | 5.920056679613887 | 10.914676311816635 | 0.379715 | 7.695414545454544 | 3.131816909090909 | 3.883018702479339 | 6.473599669421486 | 4.033451586776859 | 0.14418348122500002 | 59.21940502639337 | 9.808277152067735 | 21.574299434066237 | 46.895448857992044 | 19.698109391720433 | 4.644814251836799 | 6.848025179421586 | 4.438255219308646 |
| user_001 | Jogging | 1.446047148035e12 | -2.52854 | 0.307161 | 1.80046 | 2.7596614545454545 | -7.064272636363637 | 0.633429 | 6.3935145316 | 9.434787992100001e-2 | 3.2416562115999996 | 3.1192176299708554 | 8.707368733999472 | 5.2882014545454545 | 7.371433636363637 | 1.167031 | 4.633590611570247 | 7.0259181818181835 | 0.9637088925619838 | 27.965074623856662 | 54.338033855313235 | 1.361961354961 | 26.853786031159323 | 51.463071213056516 | 1.4078366659209935 | 5.1820638775645484 | 7.1737766352916585 | 1.1865229310556933 |
| user_001 | Jogging | 1.446047148423e12 | 3.41111 | -10.84405 | -5.99e-4 | 4.522395454545454 | -16.672220000000003 | 2.9640000909090904 | 11.635671432099999 | 117.59342040249999 | 3.5880100000000004e-7 | 11.367897439430081 | 17.70845344887635 | 1.1112854545454542 | 5.828170000000004 | 2.9645990909090902 | 2.8486870247933878 | 2.1940752892561997 | 1.3709815702479338 | 1.2349553614842967 | 33.96756554890004 | 8.788847769819004 | 9.0019158055592 | 10.105985401013 | 2.663159615259104 | 3.0003192839361614 | 3.1789912552589694 | 1.6319189977627884 |
| user_001 | Jogging | 1.446047148585e12 | 3.10454 | -5.24928 | -0.230521 | 7.291908181818181 | -0.17706772727272738 | -1.0526655454545453 | 9.638168611600001 | 27.5549405184 | 5.3139931441e-2 | 6.102970511270803 | 8.810267540587791 | 4.187368181818181 | 5.072212272727272 | 0.8221445454545453 | 2.3407064462809912 | 2.738793338842975 | 5.057014528925619 | 17.5340522901033 | 25.72733733960516 | 0.6759216536206609 | 8.01479266604553 | 10.180405784232084 | 31.71471424852974 | 2.8310409156431366 | 3.1906748164349317 | 5.631581860235163 |
| user_001 | Jogging | 1.446047148658e12 | 8.85259 | -19.58108 | -0.996927 | 5.82877 | -13.097980909090909 | 3.5980274545454547 | 78.36834970809998 | 383.4186939664 | 0.993863443329 | 21.512343134066754 | 15.243843861466813 | 3.0238199999999997 | 6.483099090909091 | 4.594954454545455 | 1.8757956198347103 | 7.861681504132232 | 4.55061688429752 | 9.143487392399999 | 42.03057382254628 | 21.11360643934712 | 5.88666845142667 | 68.0709621098762 | 23.399370359750957 | 2.426245752479882 | 8.250512839204372 | 4.837289567490348 |
| user_001 | Jogging | 1.446047148723e12 | 11.57333 | -19.58108 | 4.5212 | 11.754482727272727 | -19.581079999999996 | 3.4377791818181818 | 133.9419672889 | 383.4186939664 | 20.441249440000004 | 23.19055649818046 | 23.48316918410363 | 0.18115272727272647 | 3.552713678800501e-15 | 1.0834208181818186 | 3.3810542975206612 | 3.4684628016528944 | 2.8087840661157024 | 3.281631059834682e-2 | 1.2621774483536189e-29 | 1.1738006692697611 | 15.367657156510823 | 23.07182841418071 | 12.982727905804962 | 3.9201603483162297 | 4.803314315572187 | 3.603155270843176 |
| user_001 | Jogging | 1.446047148933e12 | -6.55217 | 0.958607 | 0.305964 | -8.673726363636364 | 1.5926342727272726 | -1.1432400909090907 | 42.930931708900005 | 0.918927380449 | 9.361396929600001e-2 | 6.628987332816756 | 10.970671818551988 | 2.1215563636363637 | 0.6340272727272727 | 1.4492040909090909 | 6.460932644628099 | 4.056886380165289 | 2.3039676280991737 | 4.501001404085951 | 0.4019905825619834 | 2.1001924971076447 | 69.60176164731864 | 24.599329097919824 | 6.676603281846297 | 8.342767025832535 | 4.959771073136322 | 2.5839123982531405 |
| user_001 | Jogging | 1.446047148956e12 | 0.805325 | 0.996927 | -7.81794 | -5.966918636363638 | 1.8991967272727273 | -2.0733782727272723 | 0.6485483556249999 | 0.993863443329 | 61.120185843600005 | 7.92228487511993 | 9.345324426929823 | 6.772243636363638 | 0.9022697272727273 | 5.744561727272728 | 4.943003884297521 | 2.234929743801653 | 2.3356376694214878 | 45.863283870267786 | 0.8140906607528016 | 32.99998943844663 | 48.60011409208167 | 10.094791644327438 | 7.651563012286325 | 6.971378206070996 | 3.177230184347278 | 2.7661458768991785 |
| user_001 | Jogging | 1.446047148981e12 | 12.79958 | -0.650847 | -9.69564 | 5.633772727272737e-2 | 1.442836727272727 | -4.118291 | 163.82924817640003 | 0.42360181740899994 | 94.00543500959998 | 16.07041645395069 | 9.496347796961764 | 12.743242272727274 | 2.093683727272727 | 5.577348999999999 | 6.552456983471075 | 1.3722480165289257 | 3.4896825041322317 | 162.3902236214234 | 4.383511549846618 | 31.10682186780099 | 75.01246971118104 | 4.620914972535286 | 18.221982671141372 | 8.660973947032806 | 2.1496313573576487 | 4.268721432834587 |
| user_001 | Jogging | 1.446047149102e12 | 3.90927 | -19.58108 | 6.24561 | 6.884320090909092 | -13.331385454545455 | 5.482689090909091 | 15.282391932899998 | 383.4186939664 | 39.0076442721 | 20.921489673811468 | 20.6720219868055 | 2.975050090909092 | 6.249694545454545 | 0.7629209090909095 | 3.4754289752066114 | 6.580958413223141 | 10.052906661157023 | 8.850923043418195 | 39.0586819114843 | 0.5820483135280998 | 18.677426649414937 | 52.092639445531944 | 133.3166367591787 | 4.321738845582289 | 7.217523082438459 | 11.546282378288636 |
| user_001 | Jogging | 1.446047149231e12 | 9.35075 | -7.51018 | 2.49022 | 8.326558181818182 | -15.567899090909092 | 4.092707272727273 | 87.4365255625 | 56.4028036324 | 6.2011956484 | 12.249103021988997 | 18.352229744497517 | 1.0241918181818175 | 8.057719090909092 | 1.6024872727272728 | 1.0755028099173556 | 3.044405785123968 | 1.612303834710744 | 1.048968880430577 | 64.92683694800085 | 2.5679654592528927 | 2.035575883434261 | 18.39229401416228 | 3.28217159657215 | 1.4267360945298402 | 4.288623790234145 | 1.8116764602356983 |
| user_001 | Jogging | 1.446047149264e12 | 11.11349 | -2.6435 | -2.0699 | 8.939681818181818 | -10.05674090909091 | 2.490221 | 123.50965998010001 | 6.98809225 | 4.28448601 | 11.609575282502801 | 14.592364505757205 | 2.173808181818183 | 7.413240909090911 | 4.5601210000000005 | 1.1815951239669422 | 5.758498429752065 | 2.2574150991735538 | 4.725442011339674 | 54.95614077621904 | 20.794703534641005 | 2.1236015528734042 | 40.66845828931668 | 6.497162255694437 | 1.4572582313623774 | 6.377182629446697 | 2.5489531685957743 |
| user_001 | Jogging | 1.44604714928e12 | 13.22111 | -0.842448 | -3.10454 | 9.89768909090909 | -6.970213454545455 | 1.1037228181818184 | 174.7977496321 | 0.7097186327039999 | 9.638168611600001 | 13.60682317355539 | 13.2681391170847 | 3.3234209090909097 | 6.127765454545455 | 4.208262818181819 | 1.6344719834710744 | 6.779212396694215 | 2.755262090909091 | 11.045126538982649 | 37.549509465920664 | 17.709475946891583 | 3.6817334965635626 | 48.05599824849062 | 9.364926510827347 | 1.918784379903996 | 6.932243377759512 | 3.060216742459159 |
| user_001 | Jogging | 1.446047149394e12 | 2.68302 | -4.29128 | 2.49022 | 5.776515454545454 | -0.11436245454545459 | -1.007377090909091 | 7.1985963204 | 18.415084038400003 | 6.2011956484 | 5.640467711741643 | 6.993932828298204 | 3.0934954545454536 | 4.176917545454546 | 3.4975970909090908 | 4.619971983471075 | 2.365723818181818 | 2.9259617520661156 | 9.569714127293382 | 17.44664018152603 | 12.233185410335734 | 27.913373627294142 | 7.4033716166812065 | 13.02444588125051 | 5.283310858476353 | 2.7209137466449036 | 3.608939717043014 |
| user_001 | Jogging | 1.446047149402e12 | 5.86361 | -6.36057 | 5.55585 | 5.1494563636363635 | -0.9643760909090912 | -2.8467090909090942e-2 | 34.3819222321 | 40.4568507249 | 30.867469222500006 | 10.281354102427365 | 6.646319206470613 | 0.7141536363636369 | 5.396193909090909 | 5.584317090909091 | 4.641190991735538 | 2.4572493553719004 | 3.33323420661157 | 0.5100154163314058 | 29.11890870450982 | 31.184597371819375 | 27.93872818168933 | 8.299004545766735 | 15.748543563853593 | 5.285709808690724 | 2.880799289392917 | 3.9684434686478265 |
| user_001 | Jogging | 1.446047149434e12 | 6.89825 | -19.58108 | 9.04299 | 4.630389999999999 | -7.005050545454545 | 2.9082616363636364 | 47.5858530625 | 383.4186939664 | 81.7756681401 | 22.644650917357946 | 10.195556118901754 | 2.2678600000000007 | 12.576029454545456 | 6.134728363636363 | 4.597170826446281 | 5.882325909090909 | 3.9169054380165282 | 5.143188979600003 | 158.15651684159488 | 37.634892095604485 | 26.575008670971535 | 50.825509825809554 | 21.491222617181755 | 5.155095408522672 | 7.129201205311122 | 4.635862661596195 |
| user_001 | Jogging | 1.446047149563e12 | 3.98591 | -15.74905 | 2.95007 | 11.92169909090909 | -19.128203636363637 | 0.5080178181818181 | 15.8874785281 | 248.0325759025 | 8.702913004900001 | 16.511298175355567 | 23.144901652304863 | 7.93578909090909 | 3.379153636363636 | 2.442052181818182 | 5.774967107438017 | 0.8791501652892587 | 3.5599889008264465 | 62.97674849539172 | 11.418679298149584 | 5.963618858722944 | 41.11842897361307 | 2.1366249748332855 | 16.233832526013103 | 6.412365318165604 | 1.4617198687960993 | 4.029123046769992 |
| user_001 | Jogging | 1.44604714962e12 | -2.33694 | -4.21464 | -0.843646 | 2.188339454545454 | -12.40473181818182 | 3.2217923636363635 | 5.461288563599999 | 17.7631903296 | 0.711738573316 | 4.892465377140241 | 13.428669191244422 | 4.525279454545454 | 8.19009181818182 | 4.065438363636363 | 6.705104636363637 | 4.739049917355373 | 2.9405300743801646 | 20.478154141731203 | 67.07760399024879 | 16.52778908852631 | 47.59374277461837 | 31.377978538409174 | 10.302701637973604 | 6.8988218396055405 | 5.601604996642406 | 3.209782179209923 |
| user_001 | Jogging | 1.446047149669e12 | -10.34589 | -0.152682 | 0.152682 | -5.040265090909091 | -4.266891727272728 | 0.9608932727272728 | 107.03743989210001 | 2.3311793124000002e-2 | 2.3311793124000002e-2 | 10.34814299661287 | 8.821005416974698 | 5.30562490909091 | 4.114209727272727 | 0.8082112727272728 | 6.073611198347108 | 6.515719066115702 | 2.7058581157024792 | 28.149655675965928 | 16.926721679985526 | 0.653205461363438 | 38.210883633728024 | 44.103016009657594 | 9.257819553824516 | 6.181495258732148 | 6.641010164851248 | 3.042666520311504 |
| user_001 | Jogging | 1.446047149782e12 | 8.00954 | 2.52974 | -14.9072 | -2.4971918181818182 | 1.1780775454545456 | -3.1916349999999998 | 64.1527310116 | 6.3995844675999995 | 222.22461184 | 17.110725505343133 | 7.3390330778954285 | 10.506731818181818 | 1.3516624545454543 | 11.715565 | 3.812711074380165 | 0.9545242231404959 | 3.9469921652892554 | 110.3914134991942 | 1.8269913910278424 | 137.254463269225 | 20.90936161935897 | 1.1652671375526948 | 36.498496284713816 | 4.572675542760384 | 1.079475399234598 | 6.041398537152951 |
| user_001 | Jogging | 1.446047149863e12 | 15.32872 | -11.18893 | -5.05888 | 8.033928999999999 | -5.155224363636363 | -3.5748378181818183 | 234.96965683840003 | 125.19215454489998 | 25.592266854400002 | 19.640623163171274 | 15.724526838982381 | 7.294791000000002 | 6.033705636363636 | 1.484042181818182 | 7.81702794214876 | 4.484109628099174 | 8.200230900826448 | 53.213975733681025 | 36.40560370628631 | 2.20238119741567 | 78.26415031479696 | 30.749846915804653 | 98.87448495723784 | 8.846702793402578 | 5.54525445005048 | 9.943565002414267 |
| user_001 | Jogging | 1.446047149871e12 | 11.15181 | -15.21256 | 16.7837 | 8.31958990909091 | -6.768160727272726 | -0.6938469090909084 | 124.36286627609998 | 231.4219817536 | 281.69258569 | 25.248315463010595 | 16.46430774422488 | 2.8322200909090895 | 8.444399272727274 | 17.477546909090908 | 7.11934505785124 | 5.128903884297521 | 8.724047438016528 | 8.021470643349092 | 71.30787907723692 | 305.46464595947316 | 68.9577918733565 | 37.06629125091457 | 114.16631974726042 | 8.304082843599074 | 6.088209199010376 | 10.684864049077106 |
| user_001 | Jogging | 1.446047149944e12 | 9.38908 | -19.58108 | 8.58315 | 6.636980909090909 | -18.389666363636362 | 5.719578181818182 | 88.1548232464 | 383.4186939664 | 73.67046392249999 | 23.350459976953342 | 22.55196135090203 | 2.752099090909091 | 1.191413636363638 | 2.8635718181818177 | 2.407844991735537 | 5.750263537190083 | 6.912224115702478 | 7.574049406182646 | 1.4194664529132273 | 8.200043557885122 | 9.657153846656074 | 41.139247568377066 | 86.3043109583581 | 3.1075961524393856 | 6.413988429080385 | 9.290011354048934 |
| user_001 | Jogging | 1.446047150016e12 | 6.51505 | -13.14327 | 3.10335 | 7.936387272727271 | -18.02736636363636 | 4.813826363636364 | 42.44587650249999 | 172.74554629289997 | 9.6307812225 | 14.994072296007511 | 20.44310503121508 | 1.4213372727272713 | 4.8840963636363615 | 1.7104763636363645 | 2.761913388429752 | 1.6195868595041334 | 1.1109719834710745 | 2.0201996428437976 | 23.85439728928593 | 2.9257293905586805 | 10.517149891811421 | 5.401965107519985 | 1.8010637061118713 | 3.2430155552836037 | 2.324212793080699 | 1.3420371478136779 |
| user_001 | Jogging | 1.446047150073e12 | 17.89618 | 0.307161 | -19.54396 | 8.793368181818183 | -10.115964454545455 | -2.2684658181818182 | 320.27325859240005 | 9.434787992100001e-2 | 381.96637248159993 | 26.501584461196295 | 16.630296665418793 | 9.102811818181818 | 10.423125454545456 | 17.275494181818182 | 2.970298512396694 | 5.750580082644627 | 4.936670809917355 | 82.86118299723059 | 108.64154424119342 | 298.4426992260339 | 17.678004749792333 | 38.80134902204815 | 65.50860297388387 | 4.204521940695795 | 6.229072886236615 | 8.09373850416999 |
| user_001 | Jogging | 1.446047150219e12 | 7.51138 | -8.12331 | -0.383802 | 6.535954545454545 | -0.10739454545454558 | -3.163767454545454 | 56.4208295044 | 65.9881653561 | 0.147303975204 | 11.070514840589123 | 10.938402494551363 | 0.975425454545455 | 8.015915454545455 | 2.7799654545454544 | 3.8171466942148764 | 3.090644190082645 | 7.9747422314049565 | 0.9514548173752075 | 64.25490057442067 | 7.728207928466115 | 19.912643683929602 | 16.446861510478875 | 100.57568271687927 | 4.4623585337722025 | 4.0554730316547385 | 10.028742828334929 |
| user_001 | Jogging | 1.446047150243e12 | 8.1245 | -15.71073 | 7.20362 | 7.567119090909089 | -4.44804 | -3.1707347272727264 | 66.00750024999999 | 246.8270371329 | 51.8921411044 | 19.09781868400944 | 13.582559288601772 | 0.5573809090909103 | 11.26269 | 10.374354727272726 | 2.450916115702479 | 5.704342760330579 | 7.738803190082644 | 0.3106734778190096 | 126.84818603609999 | 107.62723600728596 | 11.5941647847275 | 46.82268835630012 | 97.03941247664534 | 3.405020526329834 | 6.842710600069253 | 9.850858463943402 |
| user_001 | Jogging | 1.446047150268e12 | 12.41638 | -19.4278 | 10.46085 | 8.852590909090909 | -10.168218181818181 | -5.2854727272726845e-2 | 154.1664923044 | 377.43941284000005 | 109.42938272250001 | 25.318674686225187 | 17.246433333074293 | 3.5637890909090917 | 9.25958181818182 | 10.513704727272728 | 2.286550661157025 | 8.188196363636363 | 8.710745471074379 | 12.70059268448265 | 85.73985544760333 | 110.53798709227691 | 10.773763729945234 | 76.44156760443937 | 104.44351299752908 | 3.282341196454938 | 8.7430868464427 | 10.2197609070628 |
| user_001 | Jogging | 1.446047150284e12 | 10.80693 | -19.58108 | 9.11964 | 8.702792727272728 | -14.045537272727275 | 4.956656181818182 | 116.7897360249 | 383.4186939664 | 83.1678337296 | 24.15318330408851 | 17.820332107144218 | 2.1041372727272716 | 5.535542727272725 | 4.162983818181819 | 1.7776185950413228 | 8.856742892561984 | 6.789029223140495 | 4.427393662480161 | 30.64223328546196 | 17.330434270443675 | 4.639401814140121 | 82.55556567736312 | 59.70904086977294 | 2.1539270679714577 | 9.086009337292314 | 7.727162536777193 |
| user_001 | Jogging | 1.446047150324e12 | 12.14814 | -19.58108 | 4.29128 | 11.284189090909091 | -18.926151818181815 | 5.541912636363636 | 147.57730545959998 | 383.4186939664 | 18.415084038400003 | 23.439519693551745 | 23.198834022657703 | 0.8639509090909083 | 0.6549281818181854 | 1.250632636363636 | 2.00912479338843 | 6.009955454545454 | 6.8473013553719 | 0.746411173319007 | 0.42893092333967403 | 1.5640819911378585 | 5.249374220344404 | 52.14090314037559 | 61.60130984272627 | 2.2911512870922346 | 7.220865816533055 | 7.848650192404186 |
| user_001 | Jogging | 1.446047150405e12 | 1.07357 | -10.42253 | 1.07237 | 6.981863636363636 | -17.13903090909091 | 4.709316363636364 | 1.1525525448999998 | 108.6291316009 | 1.1499774169 | 10.532410054811766 | 19.27835656772833 | 5.908293636363636 | 6.716500909090909 | 3.6369463636363637 | 3.372187024793389 | 1.9901223966942168 | 1.179696619834711 | 34.90793369349504 | 45.11138446181901 | 13.227378851967769 | 14.967308917364766 | 8.682548946544332 | 2.3495476234109898 | 3.868760643586621 | 2.9466165251936554 | 1.5328234155997846 |
| user_001 | Jogging | 1.446047150648e12 | 4.98224 | -9.69444 | 13.948 | 6.37222090909091 | -2.2289466363636365 | -8.420690909090825e-2 | 24.8227154176 | 93.9821669136 | 194.546704 | 17.701739641379884 | 12.222252634351964 | 1.3899809090909097 | 7.465493363636364 | 14.032206909090908 | 6.154051157024793 | 3.024770818181818 | 7.538651479338842 | 1.9320469276371917 | 55.73359116249859 | 196.90283073953862 | 50.009996299928396 | 14.55400636162292 | 78.89689127763887 | 7.0717746216864406 | 3.8149713447970903 | 8.88239220467318 |
| user_001 | Jogging | 1.446047150697e12 | 1.53341 | -17.51178 | 5.55585 | 7.535765454545455 | -9.994035454545456 | 3.1486354545454547 | 2.3513462280999997 | 306.6624387684001 | 30.867469222500006 | 18.435868686313647 | 16.111863223352596 | 6.002355454545455 | 7.517744545454546 | 2.4072145454545457 | 3.9055057851239665 | 6.042574842975207 | 8.693645363636366 | 36.02827100271158 | 56.51648305071158 | 5.7946818678479355 | 19.9161504314891 | 41.937659552253066 | 111.7926043798458 | 4.462751441822533 | 6.475929242375419 | 10.57320218192416 |
| user_001 | Jogging | 1.446047150793e12 | 10.46204 | -19.54276 | 6.24561 | 7.661177272727273 | -19.535792727272725 | 5.656872090909091 | 109.4542809616 | 381.91946841760006 | 39.0076442721 | 23.030010717568068 | 22.099586635479316 | 2.800862727272727 | 6.967272727276708e-3 | 0.5887379090909093 | 2.9281772727272735 | 2.3704752066115735 | 2.3156854628099173 | 7.844832017025618 | 4.8542889256253815e-5 | 0.34661232560073574 | 12.87407932845424 | 9.62765954508025 | 7.460318108028584 | 3.588046728856 | 3.102847006392718 | 2.731358289940846 |
| user_001 | Jogging | 1.446047150834e12 | 8.50771 | -11.4955 | 2.8351 | 9.511003636363638 | -17.368954545454542 | 3.9045884545454546 | 72.3811294441 | 132.14652025 | 8.03779201 | 14.579624196257598 | 20.393900266321058 | 1.0032936363636384 | 5.873454545454543 | 1.0694884545454544 | 2.6957220082644624 | 2.1975576033057864 | 1.385232033057851 | 1.0065981207677728 | 34.497468297520626 | 1.1438055544060244 | 12.06560516315159 | 8.839956388241845 | 2.4539926417881213 | 3.473557997666311 | 2.9732064153438533 | 1.5665224676933687 |
| user_001 | Jogging | 1.446047151037e12 | 5.40376 | -4.55952 | -0.728685 | 4.024230909090909 | -0.13526436363636377 | -2.6272832727272726 | 29.2006221376 | 20.7892226304 | 0.530981829225 | 7.107800404993446 | 7.222132790105045 | 1.3795290909090907 | 4.424255636363636 | 1.8985982727272726 | 2.206425950413223 | 2.662786214876033 | 5.014576909090909 | 1.9031005126644622 | 19.5740379358954 | 3.6046754012029827 | 7.852155238665514 | 8.991493953473054 | 37.35349103853367 | 2.8021697376614276 | 2.9985819904536632 | 6.111750243468205 |
| user_001 | Jogging | 1.446047151109e12 | 5.74865 | -19.58108 | 6.16897 | 5.313188181818181 | -13.79123 | 2.1592725454545456 | 33.04697682249999 | 383.4186939664 | 38.056190860899996 | 21.319518325933164 | 15.60131921150684 | 0.4354618181818184 | 5.7898499999999995 | 4.009697454545455 | 1.1201566115702482 | 7.893667628099174 | 4.2361372479338835 | 0.18962699509421507 | 33.522363022499995 | 16.077673676988297 | 1.8859984245438768 | 68.41907075554805 | 20.595874900877785 | 1.3733165784129588 | 8.271582119253127 | 4.538267830447844 |
| user_001 | Jogging | 1.446047151206e12 | 1.68669 | -14.5228 | 4.7128 | 7.905035454545454 | -18.184130909090907 | 0.5498201818181818 | 2.8449231561 | 210.91171984000002 | 22.21048384 | 15.361221528123993 | 20.60780788439442 | 6.218345454545455 | 3.661330909090907 | 4.162979818181817 | 4.84134479338843 | 1.2287837190082649 | 3.097295462809917 | 38.66782019206612 | 13.405344025864448 | 17.330400966589117 | 28.15586054618888 | 3.1095243914700963 | 17.112671343026122 | 5.306209621395378 | 1.763384357271578 | 4.136746468304061 |
| user_001 | Jogging | 1.446047151312e12 | -3.33327 | 0.613724 | -1.11189 | -0.23629118181818212 | -3.733888636363637 | 0.23977381818181834 | 11.1106888929 | 0.37665714817600005 | 1.2362993721000002 | 3.567021924964297 | 5.887372545391934 | 3.096978818181818 | 4.347612636363637 | 1.3516638181818184 | 2.5582775619834717 | 6.220242198347108 | 2.9696667768595044 | 9.59127780026685 | 18.901735635868775 | 1.8269950773818517 | 10.102695203948885 | 39.87971431446006 | 10.319315675667944 | 3.178473722393955 | 6.315038742118694 | 3.212369168646086 |
| user_001 | Jogging | 1.446047151352e12 | -0.267643 | 4.21583 | -1.91661 | -1.9084504545454544 | 0.4639268181818181 | -1.063117090909091 | 7.163277544900001e-2 | 17.773222588900005 | 3.6733938920999996 | 4.638776698273911 | 3.685062453253754 | 1.6408074545454543 | 3.7519031818181823 | 0.8534929090909089 | 2.8578729834710743 | 4.62060579338843 | 2.3017529421487604 | 2.692249102891933 | 14.0767774857374 | 0.7284501458684625 | 10.69858823466386 | 22.2415387209836 | 7.617826076039722 | 3.2708696450124486 | 4.7160935869619465 | 2.760040955500429 |
| user_001 | Jogging | 1.446047151417e12 | 6.9749 | -0.344284 | 8.19995 | 2.5680608181818183 | 3.209053363636363 | -3.9232039090909097 | 48.64923001 | 0.11853147265599999 | 67.23918000249999 | 10.770651859806629 | 9.713949174801307 | 4.406839181818182 | 3.553337363636363 | 12.12315390909091 | 3.6635484710743795 | 3.071323785123967 | 6.242726438016529 | 19.42023157440794 | 12.62620641981422 | 146.9708607035062 | 19.565297848868102 | 10.906363885450785 | 60.51857971860243 | 4.423267779466681 | 3.302478445872249 | 7.779368850916019 |
| user_001 | Jogging | 1.446047151578e12 | 9.54236 | -19.58108 | 2.60518 | 4.891664545454546 | -19.56714545454545 | 4.1031581818181815 | 91.0566343696 | 383.4186939664 | 6.7869628323999995 | 21.93769110841886 | 21.159088532938316 | 4.650695454545454 | 1.3934545454549863e-2 | 1.4979781818181817 | 4.304542066115702 | 4.257987305785124 | 1.9438857024793388 | 21.628968210929752 | 1.9417155702491625e-4 | 2.2439386332033053 | 26.518021072216154 | 29.578972161291038 | 6.378130290521789 | 5.1495651342823265 | 5.43865536334957 | 2.5254960484074784 |
| user_001 | Jogging | 1.446047151635e12 | 3.29615 | -12.56846 | 4.63616 | 7.093340909090909 | -17.65113 | 3.9045881818181813 | 10.864604822499999 | 157.9661867716 | 21.493979545600002 | 13.795824409570455 | 19.577262417347413 | 3.797190909090909 | 5.0826699999999985 | 0.7315718181818189 | 2.8806734710743807 | 1.836841239669422 | 1.3944159504132232 | 14.418658800082643 | 25.833534328899983 | 0.5351973251578523 | 10.559005899553947 | 6.313381829459578 | 2.739620745666492 | 3.2494624016218356 | 2.5126443897733672 | 1.6551799737993727 |
| user_001 | Jogging | 1.446047151724e12 | 6.82161 | 2.91294 | -5.4804 | 7.260554545454545 | -3.1416661818181826 | -2.338138363636364 | 46.53436299209999 | 8.485219443599998 | 30.034784160000005 | 9.222492428606271 | 9.907660701299607 | 0.43894454545454487 | 6.054606181818182 | 3.1422616363636364 | 1.4650392561983472 | 7.173182066115704 | 4.107240454545455 | 0.19267231398429702 | 36.65825601691095 | 9.873808191362677 | 2.9706421240818184 | 51.96965262163333 | 20.13785207873389 | 1.7235550829845323 | 7.2089980317401485 | 4.487521819304491 |
| user_001 | Jogging | 1.446047151733e12 | 7.62634 | 3.94759 | -5.02056 | 7.518345454545454 | -1.7969716363636365 | -3.101061090909091 | 58.1610617956 | 15.583466808099999 | 25.206022713599996 | 9.94738917089806 | 9.691493439543757 | 0.10799454545454612 | 5.7445616363636365 | 1.9194989090909087 | 1.2918062809917357 | 7.148796347107439 | 4.224101760330578 | 1.1662821847934029e-2 | 32.999988393980864 | 3.6844760620011887 | 2.6031193905631858 | 51.68294079364623 | 20.43626016398501 | 1.6134185416571813 | 7.189084837004375 | 4.520648201749945 |
| user_001 | Jogging | 1.446047151741e12 | 7.24314 | 4.40743 | -4.21583 | 7.685561818181816 | -0.5985916363636367 | -3.7246365454545454 | 52.4630770596 | 19.425439204899998 | 17.773222588900005 | 9.468988269788913 | 9.585143130985788 | 0.44242181818181603 | 5.006021636363636 | 0.49119345454545504 | 1.2357502479338842 | 6.958460958677687 | 4.146827280991736 | 0.1957370652033039 | 25.060252623740855 | 0.24127100978829802 | 2.5189534991681444 | 49.37879148738046 | 20.294661930347736 | 1.5871211356314754 | 7.02700444623315 | 4.504959703520969 |
| user_001 | Jogging | 1.446047151781e12 | 4.48408 | 2.52974 | -1.91661 | 6.466280000000001 | 2.944294727272727 | -4.0068127272727265 | 20.106973446399998 | 6.3995844675999995 | 3.6733938920999996 | 5.493628291584715 | 8.398280777683723 | 1.9822000000000015 | 0.41455472727272724 | 2.0902027272727266 | 1.1502414876033058 | 4.31594256198347 | 2.787565033057851 | 3.929116840000006 | 0.17185562190416526 | 4.368947441098344 | 1.8744906532619081 | 24.103478716583982 | 11.197507370263382 | 1.3691203939982444 | 4.909529378319676 | 3.346267677616867 |
| user_001 | Jogging | 1.44604715191e12 | 9.77228 | -19.50444 | 1.53221 | 4.724448181818182 | -13.442863636363633 | 1.4625413636363638 | 95.4974563984 | 380.42317971359995 | 2.3476674841 | 21.86934620870272 | 15.13713907118882 | 5.047831818181819 | 6.061576363636366 | 6.96686363636363e-2 | 1.8713614049586773 | 7.98075973553719 | 4.206682272727272 | 25.480606064648764 | 36.74270801219507 | 4.8537188927685855e-3 | 5.178756076771825 | 67.91527688974624 | 23.678325446133485 | 2.2756880446958947 | 8.241072557000468 | 4.866037961846731 |
| user_001 | Jogging | 1.446047151951e12 | 10.92189 | -19.58108 | 1.8771 | 9.577193636363639 | -18.46630818181818 | 0.8528991818181818 | 119.2876811721 | 383.4186939664 | 3.52350441 | 22.49955287441286 | 21.701183277391326 | 1.344696363636361 | 1.1147718181818185 | 1.0242008181818183 | 3.7595082644628093 | 5.658104900826447 | 5.013626694214876 | 1.8082083103768523 | 1.2427162066123973 | 1.048987315964306 | 24.23546088704974 | 43.77931753685487 | 35.968189974079955 | 4.922952456306047 | 6.616594103982416 | 5.997348578670407 |
| user_001 | Jogging | 1.446047151976e12 | 4.25415 | -19.58108 | 4.67448 | 10.047488181818183 | -19.225746363636357 | 1.0549519090909092 | 18.0977922225 | 383.4186939664 | 21.850763270399998 | 20.57589000406301 | 22.60094362279498 | 5.793338181818183 | 0.35533363636364257 | 3.619528090909091 | 4.706749008264463 | 3.0827260330578525 | 4.312143818181818 | 33.56276728891241 | 0.12626199313140937 | 13.100983600880006 | 29.278619037157853 | 16.794070458794213 | 28.49283913515609 | 5.410972097244437 | 4.098056912586038 | 5.337868407440941 |
| user_001 | Jogging | 1.446047152016e12 | 4.48408 | -14.98264 | 6.05401 | 7.438222727272728 | -18.466307272727274 | 3.2914646363636364 | 20.106973446399998 | 224.4795013696 | 36.6510370801 | 16.77013750379227 | 20.591913786123246 | 2.9541427272727283 | 3.483667272727274 | 2.7625453636363635 | 4.226635619834712 | 1.3982168595041338 | 2.6770381157024796 | 8.726959253098354 | 12.135937667071085 | 7.631656886148768 | 20.821342925891145 | 3.0201815245361403 | 8.235197835964952 | 4.563040973505623 | 1.7378669467298526 | 2.8697034404211443 |
| user_001 | Jogging | 1.446047152064e12 | 1.87829 | -2.8351 | -1.53341 | 3.9789436363636366 | -11.882180000000004 | 2.9535480000000005 | 3.5279733241 | 8.03779201 | 2.3513462280999997 | 3.7305645098563835 | 13.13876244651408 | 2.100653636363637 | 9.047080000000003 | 4.4869580000000004 | 2.783764876033059 | 5.077600413223141 | 2.743226958677686 | 4.41274569996777 | 81.84965652640005 | 20.132792093764003 | 10.053849827812401 | 36.27602943311487 | 8.753824143514345 | 3.1707806338207 | 6.022958528257925 | 2.958686219171331 |
| user_001 | Jogging | 1.446047152089e12 | -1.95374 | -0.229323 | -1.72501 | 2.4844516363636364 | -7.1304620909090906 | 1.6715598181818179 | 3.8170999876000002 | 5.2589038329e-2 | 2.9756595001 | 2.616361696331186 | 8.520474343812703 | 4.438191636363636 | 6.901139090909091 | 3.396569818181818 | 2.494937132231405 | 6.901456363636364 | 3.1270625537190084 | 19.69754500108813 | 47.62572075207355 | 11.536686529783667 | 7.469635887459421 | 51.97030174191706 | 11.108772798224885 | 2.7330634620256116 | 7.209043053132438 | 3.3329825679449456 |
| user_001 | Jogging | 1.446047152121e12 | -7.1653 | 2.41478 | -1.26517 | -0.9608938181818183 | -1.619304454545455 | -0.8958999090909091 | 51.34152409 | 5.8311624484 | 1.6006551288999997 | 7.66637734965479 | 5.000751755683819 | 6.204406181818182 | 4.034084454545455 | 0.3692700909090908 | 3.5713902892561977 | 7.143729504132231 | 2.8534371157024796 | 38.49465606898367 | 16.273837386405297 | 0.13636040004000818 | 16.102131490102952 | 53.84664377135873 | 10.161223564866225 | 4.012746128289572 | 7.338027239753115 | 3.1876674175431514 |
| user_001 | Jogging | 1.446047153934e12 | 7.47306 | -7.51018 | 2.52854 | 4.94740290909091 | -6.4894690909090915 | -0.23400418181818186 | 55.8466257636 | 56.4028036324 | 6.3935145316 | 10.89233418178124 | 11.335213146620982 | 2.52565709090909 | 1.0207109090909086 | 2.762544181818182 | 3.6483468760330577 | 7.761921628099173 | 2.983284371900827 | 6.378943740859366 | 1.041850759937189 | 7.631650356497488 | 17.440279192640055 | 74.55776113851049 | 11.886500614431673 | 4.1761560306866 | 8.634683615426248 | 3.4476804687255567 |
| user_001 | Jogging | 1.446047154257e12 | 4.17751 | 0.307161 | 0.650847 | 5.1006838181818175 | -7.677398090909091 | 0.23280609090909074 | 17.4515898001 | 9.434787992100001e-2 | 0.42360181740899994 | 4.239049362466778 | 12.33218067780529 | 0.9231738181818177 | 7.984559090909091 | 0.4180409090909092 | 3.599259553719008 | 8.311389859504134 | 3.1143959008264472 | 0.8522498985763958 | 63.753183876219005 | 0.1747582016735538 | 17.226113662722206 | 86.6462533486209 | 15.967773060603179 | 4.150435358215112 | 9.308396926894604 | 3.995969602061955 |
| user_001 | Jogging | 1.44604715471e12 | 9.00587 | -2.22198 | -1.49509 | 5.100684 | -7.464894272727275 | -0.5545014545454546 | 81.1056944569 | 4.937195120399999 | 2.2352941081 | 9.395647060495621 | 11.52705378980653 | 3.9051859999999996 | 5.242914272727274 | 0.9405885454545454 | 3.9270390578512395 | 7.693831363636364 | 1.828923950413223 | 15.250477694595997 | 27.488150071167365 | 0.8847068118402974 | 21.882637782047397 | 70.36756291734724 | 7.930507014658335 | 4.67788817545347 | 8.388537591102947 | 2.81611558971899 |
| user_001 | Jogging | 1.446047154905e12 | 6.74497 | -15.40417 | 3.48655 | 4.529362181818183 | -9.478453363636364 | 0.7449061818181818 | 45.4946203009 | 237.28845338890002 | 12.1560309025 | 17.173791211968894 | 12.247786756547718 | 2.2156078181818177 | 5.925716636363637 | 2.741643818181818 | 3.307896446280992 | 8.032380694214877 | 1.6208546280991738 | 4.908918003988394 | 35.11411765447677 | 7.5166108257745785 | 17.328900067769936 | 72.46294735240768 | 4.432801456994455 | 4.162799546911902 | 8.512517098508976 | 2.1054219189973433 |
| user_001 | Jogging | 1.44604715497e12 | 5.86361 | -4.7128 | 0.650847 | 4.682644000000001 | -9.934813454545456 | 0.7449061818181818 | 34.3819222321 | 22.21048384 | 0.42360181740899994 | 7.550894509229288 | 12.548863588071583 | 1.1809659999999997 | 5.222013454545456 | 9.405918181818185e-2 | 3.3313320991735536 | 7.781240181818182 | 1.591401743801653 | 1.3946806931559994 | 27.269424519453768 | 8.847129684305791e-3 | 17.378211958186263 | 69.14624195633812 | 4.41771863226816 | 4.16871826322987 | 8.315421934955442 | 2.1018369661484595 |
| user_001 | Jogging | 1.446047155164e12 | 5.59536 | -16.13225 | 3.7722e-2 | 4.355179454545455 | -6.994599818181817 | 8.649363636363634e-2 | 31.308053529600002 | 260.24949006249994 | 1.422949284e-3 | 17.075097848662068 | 10.670622017712152 | 1.2401805454545451 | 9.13765018181818 | 4.877163636363634e-2 | 3.3595184628099175 | 7.907918900826448 | 1.9141160082644626 | 1.5380477853239332 | 83.49665084528183 | 2.3786725135867747e-3 | 16.90470911643385 | 71.7752335717931 | 6.28171303261849 | 4.111533669621817 | 8.472026532760216 | 2.506334581140054 |
| user_001 | Jumping | 1.446047227671e12 | 0.575403 | -0.727487 | 2.95007 | 2.4530965 | -6.7246185 | -0.11555499999999985 | 0.331088612409 | 0.529237335169 | 8.702913004900001 | 3.092448698439151 | 8.451269086203546 | 1.8776935 | 5.9971315 | 3.065625 | 0.93884675 | 2.99856575 | 1.5328125 | 3.52573287994225 | 35.96558622829225 | 9.398056640624999 | 1.762866439971125 | 17.982793114146126 | 4.699028320312499 | 1.3277298068399026 | 4.240612351317452 | 2.1677242260750096 |
| user_001 | Jumping | 1.446047228771e12 | 5.63368 | -19.58108 | 5.59417 | 2.6865030909090906 | -9.102216272727274 | 1.904966727272727 | 31.7383503424 | 383.4186939664 | 31.2947379889 | 21.129405630488048 | 10.699369611977787 | 2.9471769090909095 | 10.478863727272726 | 3.6892032727272728 | 2.3082646342516067 | 7.828420587603304 | 1.485286929476584 | 8.685851733478646 | 109.80658501475205 | 13.61022078750162 | 7.602895554261843 | 72.41129287122205 | 4.47538958087852 | 2.757334864368462 | 8.509482526641795 | 2.1155116593577357 |
| user_001 | Jumping | 1.446047229678e12 | 1.64837 | -0.727487 | -0.307161 | 1.7702999999999998 | -6.9388619090909085 | 0.6334268181818182 | 2.7171236568999997 | 0.529237335169 | 9.434787992100001e-2 | 1.8277606167083258 | 8.146417896575903 | 0.12192999999999987 | 6.2113749090909085 | 0.9405878181818182 | 1.3057419917355373 | 6.445413867768594 | 0.7844572066115703 | 1.4866924899999969e-2 | 38.581178261284094 | 0.8847054437120332 | 2.9860285836722325 | 51.402171643057116 | 0.9434764262345948 | 1.7280129003199693 | 7.169530782628464 | 0.9713271468638127 |
| user_001 | Jumping | 1.446047229743e12 | 2.56806 | -1.03405 | 0.267643 | 1.8504245454545454 | -6.945829272727272 | 0.5149825454545455 | 6.5949321636 | 1.0692594024999997 | 7.163277544900001e-2 | 2.7813349926876842 | 8.172391978699084 | 0.7176354545454546 | 5.911779272727272 | 0.24733954545454545 | 1.3551465785123966 | 6.545490115702479 | 0.7524711818181818 | 0.5150006456206613 | 34.949134169447795 | 6.1176850745661156e-2 | 3.0300886047082014 | 52.47526121010706 | 0.9163994449650903 | 1.7407149694042967 | 7.243981033251472 | 0.9572875456022032 |
| user_001 | Jumping | 1.44604723039e12 | 2.68302 | 0.268841 | 0.535886 | 2.156987 | -6.151554090909091 | 0.6194927272727272 | 7.1985963204 | 7.2275483281e-2 | 0.287173804996 | 2.749189991375096 | 7.848112788192466 | 0.526033 | 6.420395090909091 | 8.360672727272722e-2 | 2.190274570247934 | 6.47613232231405 | 0.7508874214876033 | 0.27671071708899997 | 41.221473123369556 | 6.990084845256189e-3 | 7.960874463777737 | 50.38179048260929 | 1.1474568828624658 | 2.8215021644113154 | 7.0980131362663235 | 1.071194138736049 |
| user_001 | Jumping | 1.446047231296e12 | 11.61165 | -19.58108 | 4.25296 | 3.6932826363636355 | -7.416123636363636 | 0.5672378181818182 | 134.8304157225 | 383.4186939664 | 18.0876687616 | 23.158945970196918 | 9.36085068307741 | 7.918367363636364 | 12.164956363636364 | 3.6857221818181816 | 3.0409216446280993 | 7.26154073553719 | 1.760200305785124 | 62.70054170550149 | 147.98616332917686 | 13.584548001546576 | 12.618763656560139 | 62.25575293517845 | 3.835529500171806 | 3.5522899173012523 | 7.890231488060312 | 1.9584507908476552 |
| user_001 | Jumping | 1.446047231555e12 | -0.919089 | -0.382604 | 0.114362 | 2.867653454545455 | -6.116717181818182 | 0.5776896363636365 | 0.8447245899210001 | 0.146385820816 | 1.3078667044000002e-2 | 1.0020923499263927 | 8.397198505817867 | 3.786742454545455 | 5.7341131818181825 | 0.46332763636363644 | 3.6160431239669424 | 7.249506231404959 | 1.7985207438016528 | 14.339418417056937 | 32.88005398190104 | 0.2146724986183141 | 15.941718766454512 | 64.06415040944202 | 4.433685351168226 | 3.99270819951252 | 8.004008396387526 | 2.1056318175712074 |
| user_001 | Jumping | 1.446047232332e12 | 1.45677 | -0.957409 | 0.420925 | 2.8606881818181815 | -6.423278727272727 | -0.7321674545454546 | 2.1221788328999995 | 0.9166319932809999 | 0.177177855625 | 1.7933177860619125 | 8.07830938399036 | 1.4039181818181816 | 5.465869727272727 | 1.1530924545454546 | 1.8216388347107435 | 6.94674379338843 | 1.2500011074380166 | 1.970986261239669 | 29.875731875516436 | 1.3296222087296614 | 5.385024996212779 | 55.70059721558134 | 2.283652852964922 | 2.3205656629823643 | 7.463283273170149 | 1.5111759834529273 |
| user_001 | Jumping | 1.446047232397e12 | 1.18853 | -2.95007 | 0.650847 | 2.84327 | -6.649717363636362 | -0.7008144545454547 | 1.4126035609000003 | 8.702913004900001 | 0.42360181740899994 | 3.246400835264956 | 8.238312707705102 | 1.6547399999999999 | 3.6996473636363616 | 1.3516614545454546 | 1.8213221900826446 | 6.764009280991735 | 1.3415265041322315 | 2.7381644675999994 | 13.687390615261481 | 1.826988687703934 | 5.383975964246953 | 53.981179779284275 | 2.438929673971659 | 2.320339622608499 | 7.347188562932374 | 1.5617072945887327 |
| user_001 | Jumping | 1.446047233692e12 | 1.91661 | -2.95007 | -0.345482 | 2.4530986363636362 | -6.485985363636365 | 0.1108783636363636 | 3.6733938920999996 | 8.702913004900001 | 0.119357812324 | 3.534920750076867 | 7.642951055439692 | 0.5364886363636363 | 3.5359153636363647 | 0.4563603636363636 | 1.7491166859504133 | 6.342802719008264 | 0.7968080991735537 | 0.287820056947314 | 12.502697458799686 | 0.20826478149831404 | 5.789309919787663 | 48.261592607761735 | 0.9506238958732367 | 2.406098485055768 | 6.947056398775077 | 0.9749994337809826 |
| user_001 | Jumping | 1.446047233755e12 | 4.75232 | -14.63776 | -3.67935 | 2.6690859090909087 | -7.51714990909091 | -9.117436363636361e-2 | 22.5845453824 | 214.2640178176 | 13.5376164225 | 15.823595660357984 | 8.689111065763383 | 2.0832340909090914 | 7.12061009090909 | 3.588175636363636 | 1.9223500247933885 | 6.688635768595042 | 1.0153291322314049 | 4.3398642775258285 | 50.70308806675636 | 12.875004397393585 | 6.1809734275535115 | 51.871071733025275 | 1.99354172880025 | 2.486156356216059 | 7.202157436006607 | 1.411928372404298 |
| user_001 | Jumping | 1.446047234273e12 | 1.11189 | -0.152682 | 1.83878 | 2.8850731818181816 | -5.9042141818181815 | -1.0387308181818182 | 1.2362993721000002 | 2.3311793124000002e-2 | 3.3811118884000004 | 2.1542337509249085 | 7.883338389741368 | 1.7731831818181816 | 5.751532181818181 | 2.8775108181818183 | 1.935018611570248 | 6.829566181818181 | 1.9438862644628097 | 3.1441785962828503 | 33.08012243849021 | 8.280068508753397 | 7.562639685647951 | 53.59971111397245 | 5.921171536456791 | 2.75002539727326 | 7.321182357650467 | 2.433345749468577 |
| user_001 | Jumping | 1.446047235892e12 | 2.79798 | -9.88604 | -0.843646 | 2.5436736363636365 | -6.792547727272727 | -0.22703800000000005 | 7.8286920804 | 97.7337868816 | 0.711738573316 | 10.308938720126141 | 8.932443619128527 | 0.2543063636363634 | 3.0934922727272722 | 0.6166079999999999 | 2.782497404958678 | 6.824814438016529 | 1.274070082644628 | 6.467172658595029e-2 | 9.569694441423344 | 0.3802054256639999 | 12.44456348689605 | 56.63568852073141 | 2.3675355543652423 | 3.5276852873939943 | 7.525668642767326 | 1.5386798089158258 |
| user_001 | Jumping | 1.446047236475e12 | 7.58802 | -18.54643 | 3.10335 | 3.8918525454545456 | -8.22433409090909 | 0.22583972727272725 | 57.578047520400006 | 343.9700657449 | 9.6307812225 | 20.277546559872572 | 9.852919901323235 | 3.6961674545454546 | 10.32209590909091 | 2.8775102727272728 | 3.0387060082644624 | 7.119976330578512 | 1.2436688099173556 | 13.661653852041026 | 106.5456639564713 | 8.280065369650984 | 13.376589391758433 | 61.73144706318171 | 2.1075806428934363 | 3.6574020002945304 | 7.856936238966288 | 1.451750888717977 |
| user_001 | Jumping | 1.44604723654e12 | 0.767005 | -6.28393 | 0.689167 | 3.000034818181818 | -7.029436818181818 | 0.4313758181818182 | 0.5882966700250001 | 39.4877762449 | 0.47495115388899994 | 6.36796859829051 | 8.41581035264782 | 2.233029818181818 | 0.7455068181818181 | 0.2577911818181818 | 2.5535269834710745 | 6.1192146776859495 | 1.1641781074380164 | 4.986422168889123 | 0.5557804159555784 | 6.645629342321485e-2 | 8.620365385624313 | 49.222535047066316 | 1.9970901490347777 | 2.9360458759400054 | 7.015877354049621 | 1.4131844002234024 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 | 2.7248254545454547 | -5.9285986363636365 | 0.25719245454545453 | 1.3354113599999998e-2 | 1.3078667044000002e-2 | 1.2362993721000002 | 1.1237135545787458 | 7.6763875816918326 | 2.6092654545454548 | 5.814236636363637 | 1.3690824545454545 | 2.8306369256198347 | 6.579058876033057 | 1.2154832396694213 | 6.808266212284298 | 33.805347663633135 | 1.8743867673442065 | 9.500612024374687 | 54.4232190659533 | 2.0968215239484818 | 3.082306283349318 | 7.37720943622677 | 1.4480405809052734 |
| user_001 | Jumping | 1.446047238481e12 | -0.152682 | -1.41725 | -1.22685 | 2.965196909090909 | -5.3712112727272725 | -9.465809090909091e-2 | 2.3311793124000002e-2 | 2.0085975625 | 1.5051609225 | 1.8807100462655055 | 7.1786868769543135 | 3.117878909090909 | 3.9539612727272724 | 1.1321919090909092 | 2.806566479338843 | 6.916658347107438 | 0.8180279834710745 | 9.721168891753916 | 15.633809746227072 | 1.2818585190109175 | 11.351055486512807 | 57.72618438699079 | 0.722933934470577 | 3.3691327499095087 | 7.597774962907943 | 0.8502552172557232 |
| user_001 | Jumping | 1.4460472401e12 | 0.996927 | 3.8919e-2 | -0.575403 | 2.8502368181818176 | -5.782284454545455 | -0.1294945454545454 | 0.993863443329 | 1.514688561e-3 | 0.331088612409 | 1.1517233801130375 | 7.488380834527844 | 1.8533098181818177 | 5.821203454545455 | 0.4459084545454546 | 2.339439314049587 | 6.414693702479339 | 1.1030548595041323 | 3.434757282169122 | 33.88640965921194 | 0.19883434983511572 | 7.295192824921782 | 51.8848540825245 | 1.7815078835544063 | 2.700961463057513 | 7.20311419335585 | 1.3347313900386124 |
| user_001 | Jumping | 1.446047240295e12 | 7.39642 | -19.58108 | 1.11069 | 3.553936818181818 | -7.583338636363636 | 4.817236363636368e-2 | 54.7070288164 | 383.4186939664 | 1.2336322760999998 | 20.960900626139612 | 9.373704248824843 | 3.842483181818182 | 11.997741363636365 | 1.0625176363636362 | 2.5554266694214878 | 7.10002667768595 | 1.1068550661157026 | 14.76467700255558 | 143.945797828711 | 1.1289437275837682 | 8.508716858026242 | 63.728283208053945 | 1.7536987839118117 | 2.91697049317031 | 7.982999637232482 | 1.3242729265192321 |
| user_001 | Jumping | 1.446047241459e12 | -0.765807 | -1.41725 | 7.6042e-2 | 2.278915636363636 | -6.360573545454546 | 0.5358855454545455 | 0.586460361249 | 2.0085975625 | 5.782385764e-3 | 1.6127120975279499 | 8.104703046272826 | 3.044722636363636 | 4.943323545454546 | 0.4598435454545455 | 2.9804321652892556 | 7.0712062975206615 | 1.907149148760331 | 9.27033593238513 | 24.4364476750453 | 0.21145608629620663 | 10.990495416205771 | 58.02346179023377 | 6.027803224777757 | 3.315191610782968 | 7.617313292115125 | 2.4551584928019934 |
| user_001 | Jumping | 1.446047242042e12 | -0.995729 | 0.460442 | 0.305964 | 2.6307664545454545 | -5.716095636363637 | 1.1420436363636361 | 0.991476241441 | 0.21200683536400003 | 9.361396929600001e-2 | 1.1389016841242268 | 7.747039606303429 | 3.6264954545454544 | 6.176537636363637 | 0.8360796363636361 | 3.649297107438016 | 6.35737238016529 | 2.1823569752066114 | 13.151469281838843 | 38.1496171734165 | 0.69902915834195 | 16.822933334173815 | 47.906882437427655 | 9.32110861503425 | 4.101576932616749 | 6.921479786680566 | 3.0530490685598637 |
| user_001 | Jumping | 1.446047243531e12 | 11.57333 | -19.58108 | 0.344284 | 4.5014933636363645 | -7.2245218181818185 | 4.817318181818176e-2 | 133.9419672889 | 383.4186939664 | 0.11853147265599999 | 22.748168997261207 | 9.614007130330195 | 7.071836636363636 | 12.356558181818182 | 0.2961108181818182 | 3.602741462809917 | 7.240955603305786 | 1.341211165289256 | 50.01087341141494 | 152.68453010065784 | 8.768161664430581e-2 | 18.025288293019972 | 62.05824709530112 | 2.6538719664479 | 4.245619895023573 | 7.877705699967543 | 1.6290708905532318 |
| user_001 | Jumping | 1.446047243855e12 | -0.535886 | -0.957409 | 1.18733 | 3.3867207272727273 | -5.573264727272727 | 0.3268666363636364 | 0.287173804996 | 0.9166319932809999 | 1.4097525289 | 1.6166503416561664 | 7.784189991593638 | 3.9226067272727274 | 4.615855727272727 | 0.8604633636363637 | 3.7680581983471075 | 6.829566041322314 | 0.9785924380165288 | 15.386843536845257 | 21.306124094996438 | 0.740397200160405 | 20.30549192921104 | 54.09496022705587 | 1.5180806496353243 | 4.506161551610311 | 7.354927615351213 | 1.2321041553518617 |
| user_001 | Jumping | 1.446047244373e12 | -0.535886 | -1.18733 | 1.64717 | 4.111323636363637 | -5.952984272727273 | -0.17478236363636365 | 0.287173804996 | 1.4097525289 | 2.7131690089 | 2.10002270054302 | 8.42056546855258 | 4.647209636363637 | 4.765654272727272 | 1.8219523636363637 | 3.491582256198347 | 6.728223553719008 | 0.8411461652892562 | 21.596557404311042 | 22.711460647163708 | 3.3195104153601327 | 18.261239277957863 | 53.18916722596664 | 1.0814781705921834 | 4.273317128175472 | 7.293090375551824 | 1.0399414265198705 |
| user_001 | Standing | 1.446047262759e12 | 1.41845 | -9.11964 | 0.305964 | 1.41845 | -9.11964 | 0.305964 | 2.0120004025 | 83.1678337296 | 9.361396929600001e-2 | 9.234362354889265 | 9.234362354889265 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_001 | Standing | 1.446047263664e12 | 0.613724 | -9.6178 | 0.229323 | 1.0387312727272728 | -9.384396363636363 | 0.3930553636363637 | 0.37665714817600005 | 92.50207684000002 | 5.2589038329e-2 | 9.640089368180412 | 9.456826268521324 | 0.4250072727272728 | 0.23340363636363826 | 0.16373236363636368 | 0.23116370721500726 | 9.653719375573999e-2 | 0.16283225243342514 | 0.18063118187107444 | 5.447725746776948e-2 | 2.6808286901950425e-2 | 8.253073051903255e-2 | 1.3798900617681253e-2 | 3.6981783747246e-2 | 0.2872816223134236 | 0.11746872186961622 | 0.19230648389288907 |
| user_001 | Standing | 1.446047263988e12 | 1.45677 | -9.08132 | -0.575403 | 0.9516397272727274 | -9.391362727272728 | 0.23629054545454545 | 2.1221788328999995 | 82.47037294239999 | 0.331088612409 | 9.215402345405707 | 9.459287967285888 | 0.5051302727272725 | 0.3100427272727284 | 0.8116935454545454 | 0.33731404958677685 | 0.2481306611570256 | 0.28309523966942146 | 0.2551565924255287 | 9.612649273471145e-2 | 0.6588464117325702 | 0.16787532121653645 | 0.12819331652524443 | 0.13775742846689407 | 0.4097259098672385 | 0.358040942526472 | 0.37115687851216506 |
| user_001 | Standing | 1.446047265736e12 | 1.03525 | -9.46452 | -0.613724 | 0.9934438181818183 | -9.373945454545453 | -0.27580827272727276 | 1.0717425625 | 89.5771388304 | 0.37665714817600005 | 9.540730503534622 | 9.43637041659947 | 4.180618181818174e-2 | 9.057454545454746e-2 | 0.3379157272727273 | 0.18526814049586776 | 0.12287735537190142 | 0.2039529008264463 | 1.7477568382148697e-3 | 8.203748284297884e-3 | 0.11418703873825621 | 5.9142881348738546e-2 | 1.964663117896333e-2 | 5.5500906570311793e-2 | 0.24319309478013257 | 0.1401664409870042 | 0.23558630386826776 |
| user_001 | Standing | 1.446047265929e12 | 0.958607 | -9.34956 | -0.230521 | 0.9795095454545454 | -9.384396363636363 | -0.32806318181818184 | 0.918927380449 | 87.41427219360001 | 5.3139931441e-2 | 9.401400933131722 | 9.44541709001559 | 2.0902545454545396e-2 | 3.483636363636222e-2 | 9.754218181818183e-2 | 0.13142985950413222 | 9.595834710743874e-2 | 0.1992023388429752 | 4.369164064793364e-4 | 1.2135722314048601e-3 | 9.514477233851243e-3 | 2.979612839872803e-2 | 1.4155768455597456e-2 | 5.6693495851969186e-2 | 0.17261555086007757 | 0.11897801669046873 | 0.23810396017699745 |
| user_001 | Standing | 1.446047266059e12 | 1.07357 | -9.31124 | -0.537083 | 1.0317647272727273 | -9.373945454545455 | -0.35593254545454545 | 1.1525525448999998 | 86.6991903376 | 0.28845814888899995 | 9.388301285716656 | 9.440500136148358 | 4.1805272727272635e-2 | 6.270545454545484e-2 | 0.18115045454545453 | 0.1111615041322314 | 9.817520661157078e-2 | 0.18495103305785127 | 1.7476808278016453e-3 | 3.931974029752103e-3 | 3.281548718202479e-2 | 2.106930566511646e-2 | 1.492473195131493e-2 | 4.429172460069797e-2 | 0.14515269775349152 | 0.12216682017354356 | 0.2104559920760109 |
| user_001 | Standing | 1.446047266189e12 | 0.767005 | -9.15796 | 3.7722e-2 | 0.9899605454545455 | -9.346076363636364 | -0.33503045454545455 | 0.5882966700250001 | 83.86823136159998 | 1.422949284e-3 | 9.190100705700074 | 9.40808410333243 | 0.22295554545454543 | 0.18811636363636453 | 0.3727524545454545 | 0.12509620661157025 | 0.11305983471074428 | 0.20490293388429756 | 4.9709175248933875e-2 | 3.5387766267768926e-2 | 0.13894439236966113 | 2.469465301667093e-2 | 1.9301314716754423e-2 | 5.478269009800525e-2 | 0.1571453245141927 | 0.13892917158305676 | 0.23405702317598856 |
| user_001 | Standing | 1.446047267548e12 | 0.805325 | -9.92436 | -3.8919e-2 | 0.28625918181818183 | -9.429683636363636 | 0.44879372727272726 | 0.6485483556249999 | 98.4929214096 | 1.514688561e-3 | 9.957057017702871 | 9.475865926635327 | 0.5190658181818182 | 0.4946763636363638 | 0.48771272727272724 | 0.4639609173553719 | 0.21155173553719064 | 0.5874721818181817 | 0.26942932360476035 | 0.24470470474049602 | 0.23786370434380164 | 0.2774613912096176 | 6.034322433538719e-2 | 0.43992543707595644 | 0.5267460405258093 | 0.24564857894029674 | 0.6632687517710724 |
| user_001 | Standing | 1.446047268131e12 | 0.268841 | -9.77108 | 0.382604 | 0.5266323636363637 | -9.450583636363636 | 0.1979701818181818 | 7.2275483281e-2 | 95.4740043664 | 0.146385820816 | 9.782262809314469 | 9.485315427162762 | 0.2577913636363637 | 0.320496363636364 | 0.1846338181818182 | 0.33823227272727274 | 0.4072706611570247 | 0.3923870495867768 | 6.645638716549589e-2 | 0.10271791910413249 | 3.40896468163967e-2 | 0.16539073699623363 | 0.23160763757513123 | 0.22108198617076258 | 0.40668259981985166 | 0.4812563117249801 | 0.4701935624514255 |
| user_001 | Standing | 1.446047269102e12 | 0.498763 | -9.38788 | 0.305964 | 0.5649523636363637 | -9.373945454545453 | 0.20493727272727272 | 0.24876453016900002 | 88.13229089439999 | 9.361396929600001e-2 | 9.40609745823766 | 9.39505555154261 | 6.618936363636369e-2 | 1.393454545454631e-2 | 0.1010267272727273 | 9.849275206611571e-2 | 6.143867768595015e-2 | 0.12604526446280992 | 4.381031858586783e-3 | 1.9417155702481723e-4 | 1.0206399623438021e-2 | 1.6186066393682193e-2 | 7.226271014274902e-3 | 2.495586758379489e-2 | 0.12722447246376065 | 8.500747622577029e-2 | 0.1579742624094029 |
| user_001 | Standing | 1.446047269296e12 | 0.728685 | -9.50284 | 0.191003 | 0.6346258181818183 | -9.391363636363636 | 0.16661700000000002 | 0.530981829225 | 90.30396806560002 | 3.6482146009e-2 | 9.5326508401826 | 9.416083143172584 | 9.405918181818174e-2 | 0.11147636363636515 | 2.438599999999999e-2 | 0.11337746280991735 | 4.0220165289256087e-2 | 0.12192832231404958 | 8.84712968430577e-3 | 1.2426979649587114e-2 | 5.946769959999995e-4 | 1.9112998952507135e-2 | 2.688614116303524e-3 | 2.452890948348835e-2 | 0.138249770171625 | 5.185184776170974e-2 | 0.15661707915642006 |
| user_001 | Standing | 1.446047269814e12 | 0.805325 | -9.38788 | 0.114362 | 0.6276583636363636 | -9.401814545454544 | 0.14571509090909093 | 0.6485483556249999 | 88.13229089439999 | 1.3078667044000002e-2 | 9.423052473432852 | 9.42527326939873 | 0.17766663636363633 | 1.3934545454544534e-2 | 3.135309090909093e-2 | 0.13712972727272724 | 5.4471404958677964e-2 | 4.5921033057851245e-2 | 3.156543367676858e-2 | 1.941715570247677e-4 | 9.8301630955372e-4 | 2.469660010670247e-2 | 5.1499592510894174e-3 | 3.4167996044695724e-3 | 0.15715151958127058 | 7.176321656036203e-2 | 5.8453396859973604e-2 |
| user_001 | Standing | 1.446047270655e12 | 0.652044 | -9.34956 | -0.767005 | 0.6973317272727272 | -9.41574909090909 | -5.982109090909093e-2 | 0.42516137793599995 | 87.41427219360001 | 0.5882966700250001 | 9.403601982302368 | 9.447406279998765 | 4.528772727272723e-2 | 6.618909090908964e-2 | 0.7071839090909091 | 0.11876135537190079 | 6.777256198347135e-2 | 0.1973020330578512 | 2.0509782415289216e-3 | 4.380995755371733e-3 | 0.5001090812770992 | 2.098084363964838e-2 | 8.945130592937738e-3 | 7.410079386978813e-2 | 0.14484765665915475 | 9.457870052468334e-2 | 0.2722146099491872 |
| user_001 | Standing | 1.446047271109e12 | 0.652044 | -9.27292 | -7.7239e-2 | 0.6590114545454546 | -9.332141818181817 | -0.5475343636363635 | 0.42516137793599995 | 85.98704532639998 | 5.965863121e-3 | 9.296137507989917 | 9.381926009361129 | 6.9674545454546655e-3 | 5.9221818181818264e-2 | 0.47029536363636354 | 0.13998007438016533 | 9.754181818181804e-2 | 0.4522431487603306 | 4.854542284297688e-5 | 3.5072237487603405e-3 | 0.2211777290578594 | 3.434468631198724e-2 | 1.3510368677986363e-2 | 0.28600870763474 | 0.18532319420943305 | 0.11623411150770828 | 0.5347978193997616 |
| user_001 | Standing | 1.446047271885e12 | 0.652044 | -9.57948 | -5.99e-4 | 0.7495870909090908 | -9.520258181818182 | -0.3385142727272727 | 0.42516137793599995 | 91.7664370704 | 3.5880100000000004e-7 | 9.60164563015825 | 9.56080406607656 | 9.754309090909086e-2 | 5.9221818181818264e-2 | 0.3379152727272727 | 0.2087035702479339 | 0.22263603305785085 | 0.15169771074380167 | 9.514654584099164e-3 | 3.5072237487603405e-3 | 0.11418673154234707 | 6.712845657103984e-2 | 6.522730094305014e-2 | 2.9760565016948907e-2 | 0.259091598804438 | 0.2553963604733829 | 0.1725125068421096 |
| user_001 | Standing | 1.446047272015e12 | 0.307161 | -9.34956 | -0.383802 | 0.7008157272727272 | -9.461036363636364 | -0.289743 | 9.434787992100001e-2 | 87.41427219360001 | 0.147303975204 | 9.362474248227603 | 9.495551423431182 | 0.3936547272727272 | 0.11147636363636337 | 9.4059e-2 | 0.20680329752066118 | 0.18969983471074334 | 0.14504701652892563 | 0.15496404430416524 | 1.2426979649586719e-2 | 8.847095481000001e-3 | 5.4621756809489865e-2 | 5.085639922824927e-2 | 2.827003845455672e-2 | 0.23371297954861187 | 0.2255136342402589 | 0.16813696337973016 |
| user_001 | Standing | 1.44604727318e12 | 0.690364 | -9.65612 | -0.15388 | 0.6172074545454543 | -9.46452 | -0.12252709090909089 | 0.476602452496 | 93.24065345439999 | 2.3679054399999996e-2 | 9.68199023761623 | 9.489000354835825 | 7.315654545454564e-2 | 0.19159999999999933 | 3.13529090909091e-2 | 0.1944520743801653 | 0.11876033057851258 | 0.14124687603305783 | 5.351880142843002e-3 | 3.671055999999974e-2 | 9.830049084628104e-4 | 6.190883812321337e-2 | 2.0106685379414013e-2 | 3.6171692128354624e-2 | 0.2488148671667619 | 0.14179804434269894 | 0.19018856992036778 |
| user_001 | Standing | 1.446047273504e12 | 0.498763 | -9.04299 | -0.307161 | 0.5545015454545456 | -9.401813636363636 | -0.24097163636363633 | 0.24876453016900002 | 81.7756681401 | 9.434787992100001e-2 | 9.061941323479754 | 9.425355606305523 | 5.573854545454554e-2 | 0.35882363636363657 | 6.618936363636369e-2 | 0.22105472727272726 | 0.14377999999999957 | 9.975949586776861e-2 | 3.106785449388439e-3 | 0.12875440201322327 | 4.381031858586783e-3 | 6.747596271471225e-2 | 3.0185444226896983e-2 | 1.7790134395435767e-2 | 0.25976135723912486 | 0.1737395873912937 | 0.13337966260054704 |
| user_001 | Standing | 1.446047273698e12 | 0.996927 | -9.50284 | 0.114362 | 0.5649526363636365 | -9.422715454545456 | -0.17129827272727274 | 0.993863443329 | 90.30396806560002 | 1.3078667044000002e-2 | 9.555674239736986 | 9.447872353105327 | 0.43197436363636355 | 8.012454545454517e-2 | 0.28566027272727273 | 0.2508241239669422 | 0.1719658677685949 | 0.15866502479338843 | 0.18660185083904124 | 6.419942784297476e-3 | 8.160179141461985e-2 | 8.260832342466494e-2 | 3.940090960833961e-2 | 3.97539387786266e-2 | 0.28741663734840567 | 0.19849662366987408 | 0.19938389799235695 |
| user_001 | Standing | 1.44604727428e12 | 0.843646 | -10.001 | 1.18733 | 0.4674097272727273 | -9.530709090909092 | -6.330463636363635e-2 | 0.711738573316 | 100.020001 | 1.4097525289 | 10.10650741365265 | 9.557781404278169 | 0.3762362727272727 | 0.47029090909090776 | 1.2506346363636363 | 0.23973968595041326 | 0.21946917355371875 | 0.30561234710743795 | 0.14155373291571074 | 0.22117353917355248 | 1.5640869936724047 | 8.573595310997745e-2 | 6.325482515229132e-2 | 0.1940719009672727 | 0.29280702366913514 | 0.25150511953495364 | 0.4405359247181468 |
| user_001 | Standing | 1.446047274345e12 | 0.268841 | -9.6178 | 0.229323 | 0.45347509090909094 | -9.520258181818182 | -3.5435363636363626e-2 | 7.2275483281e-2 | 92.50207684000002 | 5.2589038329e-2 | 9.624289135391248 | 9.547060792013365 | 0.18463409090909094 | 9.754181818181884e-2 | 0.2647583636363636 | 0.2482904545454546 | 0.19793388429752037 | 0.31954696694214874 | 3.4089747525826455e-2 | 9.514406294215004e-3 | 7.009699111540495e-2 | 8.808920780299098e-2 | 5.395218709721989e-2 | 0.1993146053286213 | 0.29679826111854324 | 0.23227610100313784 | 0.44644664331655726 |
| user_001 | Standing | 1.446047274604e12 | 0.383802 | -9.50284 | 0.152682 | 0.2479389090909091 | -9.454067272727272 | -5.285372727272728e-2 | 0.147303975204 | 90.30396806560002 | 2.3311793124000002e-2 | 9.511812857385705 | 9.47688636967962 | 0.13586309090909088 | 4.8772727272728744e-2 | 0.2055357272727273 | 0.27615973553719014 | 0.2802762809917354 | 0.31637996694214876 | 1.845877947137189e-2 | 2.378778925619978e-3 | 4.224493518552893e-2 | 0.14886588857759583 | 0.10927961653260691 | 0.1975118552372246 | 0.3858314250778387 | 0.3305746761816563 | 0.44442305884958827 |
| user_001 | Standing | 1.446047275057e12 | -0.191003 | -9.4262 | -3.8919e-2 | -4.817263636363636e-2 | -9.349557272727273 | 8.997654545454543e-2 | 3.6482146009e-2 | 88.85324643999999 | 1.514688561e-3 | 9.428215275149904 | 9.360878751218927 | 0.14283036363636364 | 7.664272727272703e-2 | 0.12889554545454543 | 0.28724402479338845 | 0.26222611570247945 | 0.2007856859504132 | 2.040051277649587e-2 | 5.874107643801615e-3 | 1.6614061638024785e-2 | 0.1588823698457934 | 0.10760016523786636 | 6.317299137085047e-2 | 0.3986005141062834 | 0.32802464120530084 | 0.2513423787801223 |
| user_001 | Standing | 1.446047276481e12 | 0.652044 | -9.4262 | 3.7722e-2 | 0.14691254545454546 | -9.4262 | 0.14571509090909093 | 0.42516137793599995 | 88.85324643999999 | 1.422949284e-3 | 9.448800493566367 | 9.432439259405239 | 0.5051314545454545 | 0.0 | 0.10799309090909093 | 0.2055362479338843 | 9.089123966942157e-2 | 0.11496090082644628 | 0.25515778637120656 | 0.0 | 1.1662507684099177e-2 | 9.67772748796018e-2 | 1.3306267893613832e-2 | 2.066963877129452e-2 | 0.3110904609267243 | 0.11535279751100028 | 0.14376939441791678 |
| user_001 | Standing | 1.44604727661e12 | 0.652044 | -9.38788 | 0.114362 | 0.20961854545454547 | -9.398330909090909 | 0.16313336363636363 | 0.42516137793599995 | 88.13229089439999 | 1.3078667044000002e-2 | 9.411191791658482 | 9.407127586327759 | 0.44242545454545446 | 1.0450909090909732e-2 | 4.877136363636363e-2 | 0.1909682479338843 | 8.36072727272727e-2 | 0.10070957024793388 | 0.19574028282975198 | 1.092215008264597e-4 | 2.3786459109504123e-3 | 7.211494891861005e-2 | 1.1758411674830887e-2 | 1.652468511798798e-2 | 0.2685422665403159 | 0.10843621016445977 | 0.12854837656690957 |
| user_001 | Standing | 1.446047277128e12 | 0.843646 | -9.50284 | 0.152682 | 0.5231484545454544 | -9.457552727272729 | 6.210718181818182e-2 | 0.711738573316 | 90.30396806560002 | 2.3311793124000002e-2 | 9.54143691652573 | 9.47373391303917 | 0.32049754545454556 | 4.5287272727271954e-2 | 9.05748181818182e-2 | 0.23625582644628104 | 5.257123966942116e-2 | 7.600708264462812e-2 | 0.1027186766423885 | 2.05093707107431e-3 | 8.203797688669424e-3 | 8.311785452996621e-2 | 5.452249061457492e-3 | 7.488935699339596e-3 | 0.28830167278385016 | 7.383934629624975e-2 | 8.653863703190383e-2 |
| user_001 | Standing | 1.446047277193e12 | 0.460442 | -9.34956 | 0.152682 | 0.5057300909090908 | -9.450585454545454 | 7.255809090909092e-2 | 0.21200683536400003 | 87.41427219360001 | 2.3311793124000002e-2 | 9.362136018136459 | 9.465855324363725 | 4.5288090909090806e-2 | 0.10102545454545364 | 8.012390909090909e-2 | 0.19445188429752067 | 6.1755371900826035e-2 | 7.347352066115703e-2 | 2.051011178190073e-3 | 1.0206142466115519e-2 | 6.419840808008265e-3 | 6.010814769423744e-2 | 6.380080194740721e-3 | 7.0123296196949675e-3 | 0.2451696304484661 | 7.987540419140751e-2 | 8.373965380687316e-2 |
| user_001 | Standing | 1.446047277258e12 | 0.307161 | -9.27292 | -0.11556 | 0.4743770909090909 | -9.426200000000001 | 4.817245454545455e-2 | 9.434787992100001e-2 | 85.98704532639998 | 1.3354113599999998e-2 | 9.27872552239374 | 9.439863721746752 | 0.1672160909090909 | 0.1532800000000023 | 0.16373245454545454 | 0.16879945454545456 | 6.523900826446277e-2 | 8.709147107438019e-2 | 2.796122105891735e-2 | 2.3494758400000707e-2 | 2.6808316671479336e-2 | 4.429063096928475e-2 | 7.314530812922642e-3 | 9.431796205738542e-3 | 0.21045339381745487 | 8.552503032985515e-2 | 9.711743512747102e-2 |
| user_001 | Standing | 1.446047278099e12 | 0.383802 | -9.34956 | -7.7239e-2 | 0.49527909090909095 | -9.443618181818183 | -6.330454545454546e-2 | 0.147303975204 | 87.41427219360001 | 5.965863121e-3 | 9.357753043969744 | 9.457485944637822 | 0.11147709090909097 | 9.405818181818226e-2 | 1.3934454545454542e-2 | 7.949088429752069e-2 | 7.759008264462883e-2 | 6.777307438016529e-2 | 1.2427141797553732e-2 | 8.846941566942232e-3 | 1.9416902347933873e-4 | 9.047890380927874e-3 | 8.23794895627359e-3 | 7.462477452530428e-3 | 9.51203993942828e-2 | 9.076314756702519e-2 | 8.638563221120991e-2 |
| user_001 | Standing | 1.446047278552e12 | 0.575403 | -9.50284 | -0.1922 | 0.4674098181818181 | -9.429683636363636 | -4.588627272727273e-2 | 0.331088612409 | 90.30396806560002 | 3.694084e-2 | 9.52218449296216 | 9.442231526747948 | 0.1079931818181819 | 7.315636363636457e-2 | 0.1463137272727273 | 9.215876033057852e-2 | 6.618909090909174e-2 | 5.922222314049586e-2 | 1.1662527319214895e-2 | 5.351853540496005e-3 | 2.1407706788438022e-2 | 1.2904862468259955e-2 | 5.113552084147344e-3 | 5.369589683709241e-3 | 0.11359957072216406 | 7.150910490383267e-2 | 7.327748415242735e-2 |
| user_001 | Standing | 1.44604727894e12 | 0.498763 | -9.38788 | -0.11556 | 0.4221221818181818 | -9.422716363636361 | -5.9821000000000006e-2 | 0.24876453016900002 | 88.13229089439999 | 1.3354113599999998e-2 | 9.401830116427812 | 9.433455159578939 | 7.664081818181823e-2 | 3.483636363636222e-2 | 5.573899999999999e-2 | 9.057520661157026e-2 | 6.333884297520695e-2 | 7.632404132231403e-2 | 5.8738150115785195e-3 | 1.2135722314048601e-3 | 3.106836120999999e-3 | 1.256615708409317e-2 | 5.659659588279576e-3 | 7.598206086597294e-3 | 0.11209887191266989 | 7.523070907734139e-2 | 8.716768946460204e-2 |
| user_001 | Standing | 1.446047279199e12 | 0.383802 | -9.46452 | 3.7722e-2 | 0.3768345454545454 | -9.433167272727273 | -4.937000000000002e-2 | 0.147303975204 | 89.5771388304 | 1.422949284e-3 | 9.47237381836718 | 9.441813943148617 | 6.9674545454545544e-3 | 3.135272727272742e-2 | 8.709200000000002e-2 | 8.139101652892565e-2 | 5.4154710743801585e-2 | 8.70917355371901e-2 | 4.854542284297533e-5 | 9.829935074380258e-4 | 7.5850164640000025e-3 | 9.215557223067626e-3 | 5.537199117655957e-3 | 9.473758131908342e-3 | 9.599769384244408e-2 | 7.441235863521567e-2 | 9.733323241271885e-2 |
| user_001 | Standing | 1.446047279264e12 | 0.268841 | -9.4262 | -0.11556 | 0.34896527272727274 | -9.4262 | -4.240272727272727e-2 | 7.2275483281e-2 | 88.85324643999999 | 1.3354113599999998e-2 | 9.430741012077524 | 9.43350089943183 | 8.012427272727274e-2 | 0.0 | 7.315727272727272e-2 | 7.8857479338843e-2 | 4.750413223140481e-2 | 8.044114876033058e-2 | 6.419899080074382e-3 | 0.0 | 5.351986552892561e-3 | 8.738954655873033e-3 | 5.050666977610866e-3 | 8.014147201404209e-3 | 9.348237617793545e-2 | 7.106804470091228e-2 | 8.95217694273533e-2 |
| user_001 | Standing | 1.446047279458e12 | 0.575403 | -9.57948 | -0.11556 | 0.39773654545454545 | -9.433167272727273 | -5.982109090909091e-2 | 0.331088612409 | 91.7664370704 | 1.3354113599999998e-2 | 9.597441315080234 | 9.44306526313639 | 0.17766645454545454 | 0.14631272727272737 | 5.573890909090909e-2 | 0.10704340495867769 | 5.288793388429689e-2 | 9.53259173553719e-2 | 3.156536907075207e-2 | 2.14074141619835e-2 | 3.1068259866446277e-3 | 1.4841089900563487e-2 | 5.720338199849679e-3 | 1.1808280995708492e-2 | 0.12182401200323148 | 7.563291743579431e-2 | 0.10866591459932821 |
| user_001 | Standing | 1.446047279652e12 | 0.307161 | -9.34956 | -0.230521 | 0.38380181818181813 | -9.454069090909092 | -0.1120760909090909 | 9.434787992100001e-2 | 87.41427219360001 | 5.3139931441e-2 | 9.357444095743347 | 9.463956302277147 | 7.664081818181812e-2 | 0.104509090909092 | 0.1184449090909091 | 0.1007095123966942 | 6.523900826446245e-2 | 0.11021065289256195 | 5.873815011578502e-3 | 1.0922150082644855e-2 | 1.4029196489553721e-2 | 1.4068810789141245e-2 | 7.166695650187808e-3 | 1.4382200098872278e-2 | 0.11861201789507353 | 8.465633851158345e-2 | 0.11992581081181931 |
| user_001 | Walking | 1.446047179961e12 | 2.41478 | -10.92069 | -0.422122 | 2.41478 | -10.92069 | -0.422122 | 5.8311624484 | 119.26147007610001 | 0.178186982884 | 11.1924447511428 | 11.1924447511428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_001 | Walking | 1.446047180025e12 | 3.48775 | -7.70178 | 0.420925 | 2.9512650000000002 | -9.311235 | -5.985000000000018e-4 | 12.1644000625 | 59.317415168400004 | 0.177177855625 | 8.465163500283087 | 9.828804125712942 | 0.5364849999999999 | 1.6094549999999996 | 0.4215235 | 0.26824249999999994 | 0.8047274999999998 | 0.21076175 | 0.28781615522499987 | 2.5903453970249988 | 0.17768206105224998 | 0.14390807761249994 | 1.2951726985124994 | 8.884103052612499e-2 | 0.3793521815048649 | 1.1380565445145945 | 0.29806212527948767 |
| user_001 | Walking | 1.446047180155e12 | 3.60271 | -4.63616 | -2.41478 | 3.1045450000000003 | -7.2132 | -0.738265 | 12.9795193441 | 21.493979545600002 | 5.8311624484 | 6.348595225567622 | 8.084041303291126 | 0.49816499999999975 | 2.5770399999999993 | 1.676515 | 0.26505 | 1.6661345833333328 | 0.6139237083333333 | 0.24816836722499974 | 6.641135161599997 | 2.810702545225 | 0.1341593312374999 | 3.8430448301256916 | 0.7790756647626735 | 0.3662776695862033 | 1.9603685444644565 | 0.882652629726255 |
| user_001 | Walking | 1.446047180867e12 | 2.8363 | -8.96635 | 0.612526 | 3.829145454545454 | -8.58315 | -0.29670972727272726 | 8.04459769 | 80.3954323225 | 0.375188100676 | 9.424182623080688 | 9.539108041248436 | 0.9928454545454541 | 0.38320000000000043 | 0.9092357272727273 | 1.4637776923455335 | 1.5073407908631777 | 0.6176389226223272 | 0.9857420966115694 | 0.14684224000000032 | 0.8267096077491654 | 3.856431482421451 | 3.619418070049718 | 0.5527401162198362 | 1.9637798966333908 | 1.902476825101877 | 0.7434649394691294 |
| user_001 | Walking | 1.446047181772e12 | 2.98958 | -10.95901 | 0.459245 | 3.8395972727272722 | -8.569218181818183 | -3.891881818181819e-2 | 8.937588576400001 | 120.09990018009998 | 0.210905970025 | 11.368746400836153 | 9.573815275666846 | 0.8500172727272721 | 2.3897918181818163 | 0.4981638181818182 | 1.1372585950413223 | 1.8580585123966946 | 1.0203968016528926 | 0.7225293639347097 | 5.711104934248751 | 0.24816718974548763 | 1.9972560146706988 | 4.208256607415629 | 1.4502039611254238 | 1.413243084069651 | 2.0514035700991724 | 1.2042441451489079 |
| user_001 | Walking | 1.446047183261e12 | 3.25783 | -10.38421 | -0.307161 | 4.146160909090909 | -8.774753636363636 | -0.3803181818181818 | 10.613456308899998 | 107.83181732409999 | 9.434787992100001e-2 | 10.887590252802545 | 9.813098538885068 | 0.8883309090909095 | 1.6094563636363635 | 7.315718181818176e-2 | 1.316192231404959 | 1.6306713223140499 | 0.43862497520661153 | 0.7891318040462817 | 2.590349786449586 | 5.351973251578504e-3 | 2.978785422953119 | 3.2734880230278747 | 0.35883803393886404 | 1.725915821514224 | 1.809278315524694 | 0.5990309123399761 |
| user_001 | Walking | 1.446047184233e12 | 0.575403 | -6.82042 | 0.267643 | 3.5748384545454552 | -8.116340909090908 | 0.20842054545454544 | 0.331088612409 | 46.51812897640001 | 7.163277544900001e-2 | 6.849879587573638 | 9.021436625002817 | 2.999435454545455 | 1.295920909090908 | 5.922245454545458e-2 | 1.6651907438016529 | 1.9701695867768594 | 0.6460616611570248 | 8.9966130459843 | 1.6794110026190057 | 3.5072991223884338e-3 | 3.910278616992638 | 4.716892291846582 | 0.5916326549642051 | 1.9774424434083127 | 2.171840761162425 | 0.7691766084354134 |
| user_001 | Walking | 1.446047185009e12 | 5.67201 | -9.08132 | 1.30229 | 2.811915363636364 | -8.774753636363636 | -0.1991684545454546 | 32.171697440100004 | 82.47037294239999 | 1.6959592440999998 | 10.786010830079858 | 9.416481422101649 | 2.860094636363636 | 0.3065663636363638 | 1.5014584545454546 | 1.2762887355371904 | 1.8564774380165294 | 0.8443138677685951 | 8.18014132895604 | 9.398293531322324e-2 | 2.254377490726025 | 2.7301153425964624 | 5.033581220307515 | 0.9013750185915839 | 1.6523060680746962 | 2.243564400748843 | 0.9494077198925569 |
| user_001 | Walking | 1.446047185592e12 | 5.05888 | -11.30389 | 0.957409 | 4.118290000000001 | -8.854876363636363 | 0.734453909090909 | 25.592266854400002 | 127.77792913210001 | 0.9166319932809999 | 12.421224898526756 | 9.898016127468361 | 0.9405899999999994 | 2.449013636363638 | 0.22295509090909094 | 1.782054132231405 | 1.4732742148760332 | 0.8367127520661157 | 0.8847095480999988 | 5.99766779109505 | 4.9708972562281004e-2 | 4.0363248511438465 | 2.844052404847483 | 0.9860011653576671 | 2.009060688765734 | 1.6864318559750593 | 0.9929759137852575 |
| user_001 | Walking | 1.446047186174e12 | 9.84892 | -9.92436 | 3.7722e-2 | 4.275055727272728 | -8.628437272727274 | -0.18523336363636364 | 97.00122516639999 | 98.4929214096 | 1.422949284e-3 | 13.98197301975955 | 9.930992956529197 | 5.573864272727271 | 1.2959227272727265 | 0.22295536363636365 | 2.105083793388429 | 1.9061979338842983 | 0.9491400082644629 | 31.067962930785512 | 1.6794157150619815 | 4.9709094174223145e-2 | 6.514026192334838 | 4.609580222217958 | 1.286088016757356 | 2.5522590370757507 | 2.1469932981306576 | 1.134058206952957 |
| user_001 | Walking | 1.446047186822e12 | 6.43841 | -10.26924 | 1.76214 | 4.160093727272727 | -9.980099090909091 | 0.7170357272727274 | 41.453123328100006 | 105.4572901776 | 3.1051373796000004 | 12.248083559696186 | 11.236332039074833 | 2.278316272727273 | 0.28914090909090895 | 1.0451042727272726 | 2.1487878099173554 | 1.5632160330578513 | 1.4099342892561983 | 5.190725038573894 | 8.360246530991727e-2 | 1.0922429408728014 | 6.734969483419675 | 4.5926103210730265 | 2.969415216736809 | 2.5951819750105534 | 2.143037638743899 | 1.7231991227762418 |
| user_001 | Walking | 1.446047187081e12 | -2.56686 | -6.05401 | 1.80046 | 2.7944973636363644 | -9.314719090909092 | 1.567050090909091 | 6.5887702596 | 36.6510370801 | 3.2416562115999996 | 6.817731554652178 | 10.290301485292778 | 5.3613573636363645 | 3.260709090909092 | 0.23340990909090897 | 2.1662055454545457 | 1.6354220661157024 | 1.1347246198347107 | 28.74415278061787 | 10.632223775537199 | 5.448018566182639e-2 | 7.208394281388505 | 4.799637820054472 | 1.7933034590091324 | 2.6848452993400764 | 2.1908075725755722 | 1.3391428075485947 |
| user_001 | Walking | 1.446047187404e12 | 1.30349 | -9.6178 | -3.79431 | 2.9268773636363634 | -9.89649272727273 | 0.9783109090909092 | 1.6990861801000001 | 92.50207684000002 | 14.396788376099998 | 10.421034084782566 | 11.359368511348634 | 1.6233873636363634 | 0.27869272727272865 | 4.772620909090909 | 3.208138504132231 | 2.1231347107438023 | 2.1525879504132233 | 2.6353865324142225 | 7.766963623471151e-2 | 22.777910341891737 | 15.854389547210962 | 6.541645332155071 | 6.480901320259005 | 3.981757092944139 | 2.5576640381713687 | 2.545761442134554 |
| user_001 | Walking | 1.446047188311e12 | 0.268841 | -6.59049 | -1.68669 | 1.3487765454545457 | -9.25201272727273 | 1.1106903636363639 | 7.2275483281e-2 | 43.4345584401 | 2.8449231561 | 6.808212473144548 | 9.785277241718822 | 1.0799355454545458 | 2.6615227272727298 | 2.797380363636364 | 1.8925786776859506 | 2.2517144628099177 | 0.9893604297520661 | 1.1662607823362074 | 7.083703227789269 | 7.825336898858317 | 4.470154434567544 | 6.05348710545417 | 1.7177182240601185 | 2.114273973393123 | 2.4603835281220223 | 1.310617497235604 |
| user_001 | Walking | 1.446047188375e12 | 6.01689 | -9.34956 | -1.53341 | 1.6170192727272725 | -9.408778181818182 | 0.9156052727272727 | 36.2029652721 | 87.41427219360001 | 2.3513462280999997 | 11.223572679579352 | 10.056389478817382 | 4.399870727272727 | 5.921818181818139e-2 | 2.4490152727272725 | 2.14720352892562 | 2.123768347107438 | 1.1936296694214876 | 19.35886241671144 | 3.5067930578511894e-3 | 5.997675806051437 | 5.997615329821671 | 5.858261339012097 | 2.2592500544313614 | 2.4490029256457966 | 2.4203845436236153 | 1.503080188955786 |
| user_001 | Walking | 1.446047189217e12 | -0.152682 | -11.53382 | 2.91174 | 1.648372090909091 | -10.011453636363637 | 1.9398018181818182 | 2.3311793124000002e-2 | 133.0290037924 | 8.4782298276 | 11.896661103567 | 10.591852408482149 | 1.801054090909091 | 1.5223663636363636 | 0.9719381818181818 | 1.4004332396694215 | 1.1701958677685953 | 0.8126441322314051 | 3.2437958383803727 | 2.3175993451314048 | 0.9446638292760331 | 2.8743965742624247 | 1.9556462032567998 | 0.9955235722866645 | 1.6954045459011913 | 1.3984442081315935 | 0.9977592757206843 |
| user_001 | Walking | 1.446047189346e12 | -0.842448 | -5.97737 | 7.6042e-2 | 1.8155880909090911 | -9.464518181818182 | 1.995540181818182 | 0.7097186327039999 | 35.7289521169 | 5.782385764e-3 | 6.036924145238865 | 10.046823549952526 | 2.658036090909091 | 3.487148181818182 | 1.919498181818182 | 1.3383605454545455 | 1.3598966942148765 | 0.8006093388429751 | 7.065155860575281 | 12.160202441957853 | 3.6844732700033065 | 2.5872731488798784 | 2.843621813011271 | 1.0801741658893318 | 1.6085002794155425 | 1.686304187568563 | 1.0393142767658547 |
| user_001 | Walking | 1.446047191677e12 | 0.537083 | -8.50651 | -0.307161 | 2.3903937272727274 | -9.610832727272728 | 2.0234110909090908 | 0.28845814888899995 | 72.36071238010001 | 9.434787992100001e-2 | 8.528981088553897 | 10.3080660888608 | 1.8533107272727274 | 1.1043227272727272 | 2.330572090909091 | 1.2826233140495868 | 1.4156352066115703 | 0.9757431983471075 | 3.4347606518241656 | 1.2195286859710741 | 5.431566270924372 | 2.484879298377201 | 2.485166069981818 | 1.4304228509468984 | 1.576349992348527 | 1.5764409503631331 | 1.1960028641048057 |
| user_001 | Walking | 1.446047191871e12 | -0.765807 | -7.08866 | 0.842448 | 2.0455110909090912 | -8.945452727272729 | 1.6785274545454547 | 0.586460361249 | 50.2491005956 | 0.7097186327039999 | 7.179504132567444 | 9.599315317951014 | 2.8113180909090913 | 1.8567927272727287 | 0.8360794545454547 | 1.8520439338842976 | 1.8403253719008268 | 1.0935538099173556 | 7.903509408272738 | 3.4476792320528977 | 0.6990288543130251 | 4.646339945674888 | 4.588313531416454 | 1.6593189163133457 | 2.1555370434476155 | 2.142034904341303 | 1.28814553382502 |
| user_001 | Walking | 1.446047192259e12 | 1.38013 | -8.96635 | 1.95374 | 1.9862877272727275 | -9.130086363636364 | 1.1490115454545455 | 1.9047588169000003 | 80.3954323225 | 3.8170999876000002 | 9.279940254495177 | 9.802935927818352 | 0.6061577272727274 | 0.1637363636363638 | 0.8047284545454545 | 2.306503066115703 | 1.7801533057851238 | 0.9453408016528926 | 0.3674271903324382 | 2.6809596776859554e-2 | 0.6475878855551156 | 8.746389046668401 | 4.834757295079263 | 1.3251393449103732 | 2.957429466051287 | 2.1988081533138044 | 1.151146969292094 |
| user_001 | Walking | 1.446047192713e12 | 3.48775 | -8.46819 | 2.72014 | 2.7074066363636367 | -9.342589090909089 | 2.371776363636364 | 12.1644000625 | 71.7102418761 | 7.399161619599999 | 9.553732441208515 | 10.136127996634109 | 0.7803433636363635 | 0.8743990909090886 | 0.348363636363636 | 0.9355228181818181 | 1.186662809917355 | 0.8322783471074383 | 0.6089357651713138 | 0.7645737701826406 | 0.1213572231404956 | 1.5311013897875918 | 2.217004014144252 | 1.0647209232821324 | 1.237376818025775 | 1.4889607161185456 | 1.0318531500567958 |
| user_001 | Walking | 1.446047192907e12 | 7.7239e-2 | -8.16163 | 0.420925 | 2.6307662727272727 | -9.30426818181818 | 2.0965664545454548 | 5.965863121e-3 | 66.61220425690001 | 0.177177855625 | 8.172842099028099 | 10.023297609841405 | 2.5535272727272726 | 1.1426381818181799 | 1.6756414545454548 | 0.840830570247934 | 1.3127081818181816 | 1.100519958677686 | 6.520501532561983 | 1.3056220145487558 | 2.8077742841912072 | 1.316778352532375 | 2.4672248687925626 | 1.6075270925270289 | 1.1475096306926469 | 1.5707402295709378 | 1.2678829175152684 |
| user_001 | Walking | 1.446047195044e12 | 6.89825 | -11.80206 | -0.728685 | 4.52936309090909 | -9.527226363636366 | 0.46272772727272726 | 47.5858530625 | 139.2886202436 | 0.530981829225 | 13.689611211985715 | 10.874214765787855 | 2.36888690909091 | 2.274833636363635 | 1.1914127272727273 | 2.0290765454545463 | 1.1739958677685949 | 1.149926256198347 | 5.611625188062285 | 5.174868073131398 | 1.419464286707438 | 6.536308023397742 | 2.107853223673479 | 2.4247353765251414 | 2.556620430059523 | 1.4518447656941422 | 1.557156182444504 |
| user_001 | Walking | 1.446047195367e12 | -0.689167 | -5.70913 | 1.41725 | 3.250857909090909 | -8.875780909090908 | 0.6264598181818182 | 0.47495115388899994 | 32.5941653569 | 2.0085975625 | 5.922644179189646 | 9.73663651646609 | 3.940024909090909 | 3.166650909090908 | 0.7907901818181817 | 1.6598082479338843 | 1.578417024793388 | 0.9950606942148762 | 15.523796284256825 | 10.027677980046274 | 0.6253491116600329 | 4.28074090199422 | 4.0350131294623575 | 1.3578484293102449 | 2.068995143057185 | 2.008734210756206 | 1.1652675355085822 |
| user_001 | Walking | 1.446047196079e12 | 4.94392 | -8.46819 | 1.22565 | 3.595740636363636 | -8.809589090909089 | 0.3756373636363637 | 24.442344966400004 | 71.7102418761 | 1.5022179224999999 | 9.882044564005973 | 9.982507618675971 | 1.3481793636363641 | 0.34139909090908915 | 0.8500126363636362 | 1.7785685785123972 | 1.1999639669421482 | 1.4507888016528925 | 1.8175875965349517 | 0.11655333927355252 | 0.7225214819778593 | 7.081309334228584 | 2.3297273267289245 | 3.3739169697874605 | 2.661072966723871 | 1.526344432534454 | 1.8368225199478203 |
| user_001 | Walking | 1.446047196533e12 | -1.76214 | -6.66714 | 0.344284 | 2.8084324545454553 | -8.987257272727273 | 0.6961345454545455 | 3.1051373796000004 | 44.4507557796 | 0.11853147265599999 | 6.904666873344144 | 9.701824413344516 | 4.570572454545456 | 2.3201172727272734 | 0.35185054545454547 | 1.2557032892561986 | 1.4583886776859503 | 1.0045616694214876 | 20.890132562249672 | 5.382944159207441 | 0.12379880633666117 | 3.183751993088845 | 3.0256084847150273 | 1.6856977704544505 | 1.7843071465106126 | 1.7394276313532067 | 1.2983442418921303 |
| user_001 | Walking | 1.446047196855e12 | 4.10087 | -12.56846 | 2.75846 | 2.508837 | -9.614316363636364 | 0.22583909090909088 | 16.817134756899996 | 157.9661867716 | 7.609101571599999 | 13.505273899484601 | 10.186220027020314 | 1.5920329999999994 | 2.9541436363636357 | 2.532620909090909 | 1.4302031735537193 | 2.04206132231405 | 1.1198392231404959 | 2.534569073088998 | 8.726964624267765 | 6.414168669164462 | 3.542454035813926 | 5.100414254760933 | 2.3829433887385965 | 1.882140811898495 | 2.2584096738105184 | 1.5436785250623255 |
| user_002 | Jogging | 1.446034900214e12 | -12.56846 | -11.8787 | -0.767005 | -8.356712727272727 | -6.796032454545454 | -1.251235818181818 | 157.9661867716 | 141.10351369 | 0.5882966700250001 | 17.310632487914038 | 12.83587713807946 | 4.211747272727273 | 5.082667545454546 | 0.4842308181818179 | 6.308627701856225 | 4.529697224016136 | 2.365780258618654 | 17.73881508932562 | 25.83350937761694 | 0.23447948527703277 | 55.93649891061648 | 28.580764721688173 | 8.90295637602717 | 7.479070725071162 | 5.346098083807308 | 2.983782226642415 |
| user_002 | Jogging | 1.446034900667e12 | 0.230521 | -0.229323 | 1.91542 | -9.39136081818182 | -6.583527272727272 | -1.310457 | 5.3139931441e-2 | 5.2589038329e-2 | 3.6688337763999996 | 1.9428233955174616 | 12.346907069341832 | 9.62188181818182 | 6.354204272727272 | 3.2258769999999997 | 5.931096528925619 | 4.556951537190082 | 2.00754094214876 | 92.58060972305788 | 40.37591193954552 | 10.406282419128997 | 44.472090029696176 | 27.521761508496457 | 6.616798714486936 | 6.668739763230844 | 5.246118708959649 | 2.5723138833522894 |
| user_002 | Jogging | 1.446034901479e12 | 3.2195 | -2.03038 | -3.44943 | 3.863981818181818 | -0.6543304545454546 | -2.5018709090909095 | 10.36518025 | 4.1224429444 | 11.8985673249 | 5.136749022416805 | 4.770432523840446 | 0.6444818181818182 | 1.3760495454545456 | 0.9475590909090905 | 3.242975231404958 | 1.182546305785124 | 1.6772268595041324 | 0.41535681396694213 | 1.8935123515456616 | 0.8978682307644621 | 15.171435204052345 | 1.8653871867810379 | 3.1895666684273536 | 3.8950526574171427 | 1.3657917801704027 | 1.7859357962780615 |
| user_002 | Jogging | 1.446034901487e12 | 2.4531 | -3.02671 | -2.41478 | 3.8395963636363635 | -0.8668343636363637 | -2.578511818181818 | 6.01769961 | 9.1609734241 | 5.8311624484 | 4.583648708452689 | 4.894646629782785 | 1.3864963636363634 | 2.1598756363636364 | 0.16373181818181815 | 2.7435442396694216 | 1.2234004545454547 | 1.544531 | 1.922372166376859 | 4.665062764557224 | 2.6808108285123956e-2 | 11.042772225629728 | 2.0235072717044216 | 2.9524234973187875 | 3.3230666899160672 | 1.4225003591227743 | 1.7182617662390056 |
| user_002 | Jogging | 1.446034901511e12 | -5.05768 | -3.75479 | 0.919089 | 1.9619021818181819 | -1.8875475454545454 | -1.6274715454545452 | 25.580126982400003 | 14.098447944099998 | 0.8447245899210001 | 6.365791350368076 | 5.146547077019307 | 7.0195821818181825 | 1.8672424545454545 | 2.546560545454545 | 2.8376038925619835 | 1.3266429338842978 | 1.7925044545454545 | 49.274534007299316 | 3.4865943840569336 | 6.48497061166575 | 12.369201859800137 | 2.4836913994747216 | 4.655043712006266 | 3.5169876115505634 | 1.5759731595032707 | 2.157555031049328 |
| user_002 | Jogging | 1.446034901705e12 | -10.88237 | -9.69444 | 0.650847 | -13.599630000000001 | -14.836330000000002 | 1.3406110000000002 | 118.4259768169 | 93.9821669136 | 0.42360181740899994 | 14.588754077984488 | 20.226008797532412 | 2.7172600000000013 | 5.141890000000002 | 0.6897640000000003 | 1.0017113223140504 | 1.4735917355371904 | 1.4628225206611574 | 7.3835019076000075 | 26.43903277210002 | 0.47577437569600034 | 1.8488825679129242 | 4.517931804138544 | 3.464271909362718 | 1.3597362126210084 | 2.125542708142686 | 1.8612554659053975 |
| user_002 | Jogging | 1.446034901818e12 | -4.9044 | 4.25415 | -4.02423 | -2.5459604545454546 | -0.9399914545454545 | -3.32749890909091 | 24.05313936 | 18.0977922225 | 16.1944270929 | 7.6384133611241545 | 6.456276805127437 | 2.3584395454545453 | 5.194141454545455 | 0.6967310909090902 | 3.4811313636363637 | 5.85255605785124 | 2.658035719008265 | 5.562237089563842 | 26.97910544982757 | 0.48543421303937095 | 19.643839108268764 | 36.150759753165694 | 9.796203638719605 | 4.432137081394117 | 6.012550187163987 | 3.1298887582020556 |
| user_002 | Jogging | 1.446034901891e12 | -2.68182 | -7.6042e-2 | 0.995729 | -2.866457272727273 | 0.28625900000000004 | -0.9202861818181817 | 7.192158512400001 | 5.782385764e-3 | 0.991476241441 | 2.861715768486626 | 3.8984117439855948 | 0.1846372727272727 | 0.36230100000000004 | 1.9160151818181816 | 0.8411455785123967 | 2.412912652892562 | 2.5782283223140494 | 3.409092248016528e-2 | 0.13126201460100004 | 3.6711141769577598 | 1.2003020330379224 | 8.579598918568538 | 7.813035155574325 | 1.095582964926857 | 2.9290952389037366 | 2.795180701774811 |
| user_002 | Jogging | 1.446034902086e12 | -12.18526 | -5.82409 | -3.90927 | -15.679373636363636 | -11.188933636363636 | -7.281458181818183 | 148.4805612676 | 33.9200243281 | 15.282391932899998 | 14.059977863730795 | 20.738969766811962 | 3.494113636363636 | 5.364843636363636 | 3.3721881818181836 | 1.291173578512396 | 2.5475080991735535 | 2.0585292561983475 | 12.208830103822311 | 28.7815472426314 | 11.371653133594227 | 3.1528208578917405 | 9.549293706104132 | 6.302081652987829 | 1.7756184437800089 | 3.0901931502907924 | 2.5103947205544848 |
| user_002 | Jogging | 1.446034902093e12 | -10.84405 | -5.36425 | -3.0279 | -15.156823636363638 | -10.258795454545453 | -6.63698 | 117.59342040249999 | 28.7751780625 | 9.16817841 | 12.471438444501901 | 19.607507902411463 | 4.312773636363639 | 4.894545454545453 | 3.6090800000000005 | 1.361796363636363 | 2.6593016528925615 | 1.946102314049587 | 18.600016438513247 | 23.956575206611554 | 13.025458446400004 | 3.7071176739448544 | 10.506175181325467 | 5.351528005717207 | 1.925387668482598 | 3.241323060314332 | 2.313336984902374 |
| user_002 | Jogging | 1.446034902134e12 | -4.7128 | -1.53221 | -3.8919e-2 | -11.13319181818182 | -5.541913636363636 | -3.292661363636364 | 22.21048384 | 2.3476674841 | 1.514688561e-3 | 4.955770980650841 | 12.951905235740183 | 6.42039181818182 | 4.009703636363636 | 3.253742363636364 | 3.776608512396695 | 4.219351074380165 | 2.819235206611571 | 41.22143109897605 | 16.077723251467763 | 10.586839368921954 | 18.97191993703419 | 18.784659065863558 | 9.502431067894996 | 4.3556767484553065 | 4.334127255384129 | 3.0826013475464187 |
| user_002 | Jogging | 1.446034902207e12 | 1.57173 | 1.22685 | -1.15021 | -1.2779061818181818 | 1.5125097272727273 | -0.7844235454545454 | 2.4703351929000004 | 1.5051609225 | 1.3229830441 | 2.3018425574960597 | 3.8283394853389723 | 2.849636181818182 | 0.28565972727272726 | 0.36578645454545455 | 5.201743809917356 | 3.6169951900826445 | 1.362746347107438 | 8.120426368727308 | 8.160147978552892e-2 | 0.13379973032893389 | 28.055339392748365 | 15.483652908929882 | 3.2315638527796566 | 5.296729122085475 | 3.9349273066893984 | 1.7976550983933643 |
| user_002 | Jogging | 1.446034902231e12 | -1.57053 | -0.344284 | -1.11189 | -0.11784572727272725 | 1.8573923636363632 | -1.1467244545454547 | 2.4665644809 | 0.11853147265599999 | 1.2362993721000002 | 1.9548389513348663 | 2.9538514725909635 | 1.4526842727272726 | 2.201676363636363 | 3.483445454545464e-2 | 3.776608099173554 | 2.949714570247934 | 0.648277909090909 | 2.110291596229165 | 4.847378810195039 | 1.2134392234793455e-3 | 17.801429361908134 | 11.44855536925793 | 0.6513529155226738 | 4.219174014177199 | 3.383571392664551 | 0.8070643812749227 |
| user_002 | Jogging | 1.446034902256e12 | -3.83143 | -3.06503 | 0.765807 | -0.5184667272727271 | 0.7217173636363636 | -0.955123 | 14.6798558449 | 9.3944089009 | 0.586460361249 | 4.965956615502093 | 3.1629018655347987 | 3.312963272727273 | 3.7867473636363638 | 1.72093 | 3.1571484297520653 | 2.450917454545454 | 0.6970493388429753 | 10.975725646439804 | 14.339455596006951 | 2.9616000649000003 | 12.507239421669167 | 7.526855335243767 | 0.6687449028157296 | 3.5365575665707984 | 2.743511497195477 | 0.8177682451744686 |
| user_002 | Jogging | 1.446034902345e12 | -15.97897 | -10.69077 | 6.39889 | -5.165675545454545 | -10.286663636363636 | 1.2361025454545453 | 255.3274822609 | 114.2925631929 | 40.945793232099994 | 20.26242430426083 | 12.469106604092127 | 10.813294454545456 | 0.4041063636363642 | 5.162787454545454 | 3.1954709173553724 | 5.691991082644628 | 2.7904146694214877 | 116.92733696070351 | 0.16330195313140541 | 26.65437430081193 | 25.07221924340757 | 44.56485109069431 | 11.683520211182929 | 5.007216716241426 | 6.675691057163618 | 3.4181164712722896 |
| user_002 | Jogging | 1.446034902498e12 | -6.82042 | -10.001 | -1.83997 | -16.61648272727273 | -13.056175454545455 | -2.1430520000000004 | 46.51812897640001 | 100.020001 | 3.3854896009 | 12.244330099164266 | 21.356799196416535 | 9.796062727272728 | 3.0551754545454557 | 0.3030820000000003 | 5.281235619834711 | 2.6536024793388435 | 0.7819239338842976 | 95.96284495666201 | 9.334097058057031 | 9.185869872400018e-2 | 33.41177882625298 | 8.344735970507742 | 0.8624901692798557 | 5.780292278618182 | 2.888725665498152 | 0.9287034883534441 |
| user_002 | Jogging | 1.446034902563e12 | -6.13065 | 2.56806 | -0.843646 | -5.507074545454545 | -5.120387272727272 | -1.7145605454545458 | 37.5848694225 | 6.5949321636 | 0.711738573316 | 6.700114936284003 | 8.609060382124232 | 0.6235754545454553 | 7.688447272727272 | 0.8709145454545458 | 6.654433884297521 | 5.132704710743802 | 0.6333935123966944 | 0.38884634751157116 | 59.112221465507425 | 0.7584921454842982 | 56.966221893599545 | 28.942580369569132 | 0.4629910177542008 | 7.5475970940160515 | 5.379830886707233 | 0.6804344331044695 |
| user_002 | Jogging | 1.446034902636e12 | -1.91542 | -3.21831 | -0.805325 | -4.4550090909090905 | 2.167436727272727 | -3.0209355454545457 | 3.6688337763999996 | 10.357519256099998 | 0.6485483556249999 | 3.830783390916928 | 6.343693907563632 | 2.5395890909090904 | 5.385746727272727 | 2.215610545454546 | 0.8652156198347104 | 4.714664330578511 | 1.6072363801652896 | 6.44951275066446 | 29.006267810328886 | 4.90893008912939 | 1.2775306625186316 | 27.85260437570646 | 3.7260181570074264 | 1.1302790197639836 | 5.277556667218881 | 1.9302896562452554 |
| user_002 | Jogging | 1.446034902724e12 | -10.72909 | -13.06663 | -2.18486 | -2.8072346363636367 | -8.112857272727272 | -0.1643308181818182 | 115.11337222809999 | 170.7368195569 | 4.7736132196000005 | 17.047692072670717 | 9.016976973428022 | 7.921855363636363 | 4.953772727272728 | 2.0205291818181816 | 3.2974469504132227 | 5.105151702479339 | 2.174439933884298 | 62.755792402374205 | 24.539864233471082 | 4.0825381745788505 | 15.810664296136576 | 26.721917705194425 | 5.090688727973939 | 3.9762626040210893 | 5.1693246856039545 | 2.256255466026385 |
| user_002 | Jogging | 1.446034902838e12 | -14.75272 | -6.20729 | -5.63368 | -16.456233636363635 | -10.419043636363636 | -6.685749999999999 | 217.6427473984 | 38.530449144100004 | 31.7383503424 | 16.967956473450183 | 20.801511960308556 | 1.7035136363636347 | 4.211753636363635 | 1.0520699999999987 | 3.477331760330579 | 2.66595388429752 | 2.398978809917355 | 2.9019587092768537 | 17.738868693422305 | 1.1068512848999974 | 16.279098479944164 | 8.922266403834108 | 8.364879422591338 | 4.034736482094483 | 2.98701630458123 | 2.8922101276690353 |
| user_002 | Jogging | 1.446034902895e12 | -6.28393 | -1.49389 | 1.49389 | -12.14694 | -4.357465454545455 | -3.3414327272727267 | 39.4877762449 | 2.2317073320999996 | 2.2317073320999996 | 6.6295694361775865 | 13.545898291325244 | 5.863010000000001 | 2.863575454545455 | 4.835322727272727 | 3.6717825619834716 | 4.083489504132231 | 2.464850578512396 | 34.37488626010001 | 8.20006438387521 | 23.380345876880163 | 17.17902520682983 | 17.189657202903156 | 8.621513752262207 | 4.144758763405879 | 4.14604114824047 | 2.9362414328972006 |
| user_002 | Jogging | 1.446034903033e12 | -2.60518 | -5.2876 | 1.49389 | 3.620127363636364 | -2.727110272727273 | -1.8957118181818187 | 6.7869628323999995 | 27.958713760000002 | 2.2317073320999996 | 6.080903216176032 | 6.2024957720086515 | 6.225307363636364 | 2.5604897272727274 | 3.3896018181818186 | 4.484111008264463 | 2.532939611570248 | 2.103498991735538 | 38.75445177174513 | 6.556107643469166 | 11.489400485821491 | 24.000768885446554 | 7.253680017838164 | 5.325640246205652 | 4.89905795898013 | 2.693265679029487 | 2.307734873464812 |
| user_002 | Jogging | 1.446034903138e12 | -12.41518 | -11.72542 | -7.7239e-2 | -12.181777272727272 | -11.728901818181818 | 0.26764363636363636 | 154.1366944324 | 137.4854741764 | 5.965863121e-3 | 17.07712313218831 | 17.103313548749245 | 0.23340272727272726 | 3.4818181818181415e-3 | 0.34488263636363636 | 5.954216669421488 | 3.747789917355372 | 1.2268833388429752 | 5.4476833098347104e-2 | 1.2123057851239389e-5 | 0.11894403286513223 | 41.42377563739363 | 22.08025245799609 | 2.545374521742775 | 6.4361304863554185 | 4.698962913026245 | 1.595422991479932 |
| user_002 | Jogging | 1.446034903162e12 | -15.82569 | -12.79839 | -0.345482 | -13.836517272727274 | -12.927282727272729 | 0.260676 | 250.4524639761 | 163.7987865921 | 0.119357812324 | 20.35609511621824 | 19.101228338167484 | 1.9891727272727255 | 0.1288927272727296 | 0.606158 | 4.112941603305784 | 2.917411074380166 | 1.1961640578512398 | 3.956808138925613 | 1.6613335143802255e-2 | 0.36742752096399994 | 25.41021847834294 | 18.272074295056942 | 2.3195890725029122 | 5.040854935260778 | 4.274584692699039 | 1.5230197216395172 |
| user_002 | Jogging | 1.446034903332e12 | -3.90807 | 2.68302 | -4.59904 | -3.5248700000000004 | -3.507452363636365 | -2.418262727272727 | 15.2730111249 | 7.1985963204 | 21.151168921599997 | 6.604754073158213 | 6.2858962299151155 | 0.38319999999999954 | 6.190472363636365 | 2.1807772727272727 | 4.531614380165291 | 4.087922247933886 | 1.003929388429752 | 0.14684223999999965 | 38.3219480849456 | 4.755789513243801 | 26.03798165480255 | 17.937086675501124 | 1.3010746092393464 | 5.1027425620741 | 4.23521979069577 | 1.1406465750789534 |
| user_002 | Jogging | 1.446034903364e12 | -3.06503 | 3.90927 | -2.52974 | -2.817686363636364 | 0.32109581818181826 | -3.007001818181818 | 9.3944089009 | 15.282391932899998 | 6.3995844675999995 | 5.574619745005035 | 5.520307117261362 | 0.24734363636363632 | 3.5881741818181814 | 0.47726181818181823 | 2.2561485950413216 | 4.613006 | 1.049850388429752 | 6.117887444958676e-2 | 12.874993959066575 | 0.22777884309421492 | 10.156513458621633 | 22.844478304031163 | 1.672854066725234 | 3.186928530516747 | 4.779589763152394 | 1.2933885984982372 |
| user_002 | Jogging | 1.446034903559e12 | -19.58108 | -10.42253 | -8.81427 | -15.08018545454545 | -16.330821818181818 | -7.13166 | 383.4186939664 | 108.6291316009 | 77.69135563290001 | 23.86920989895141 | 23.72028927423204 | 4.50089454545455 | 5.908291818181818 | 1.6826100000000004 | 5.996020479338842 | 5.813287438016529 | 3.578356950413223 | 20.258051709302517 | 34.90791220879421 | 2.8311764121000014 | 37.15895341889142 | 43.90340861610187 | 14.47403434508205 | 6.095814418015974 | 6.625964730973284 | 3.80447556768105 |
| user_002 | Jogging | 1.446034903656e12 | -3.02671 | -2.10702 | 1.83878 | -12.244483636363638 | -4.859112727272729 | -3.811728818181818 | 9.1609734241 | 4.439533280399999 | 3.3811118884000004 | 4.120875949710206 | 13.973353179277263 | 9.217773636363638 | 2.752092727272729 | 5.650508818181818 | 5.437367190082646 | 4.621873553719009 | 3.1761519338842983 | 84.96735081124054 | 7.574014379507447 | 31.928249904350483 | 40.0247632765042 | 23.596950291099475 | 14.544418632169284 | 6.326512726337015 | 4.857669224134089 | 3.81371454518679 |
| user_002 | Jogging | 1.446034903664e12 | -1.91542 | -1.37893 | 2.98839 | -10.638514545454544 | -4.179798181818182 | -2.759661545454545 | 3.6688337763999996 | 1.9014479449 | 8.9304747921 | 3.8079858867122915 | 12.21590272555592 | 8.723094545454545 | 2.8008681818181818 | 5.748051545454545 | 5.953899504132232 | 4.342230082644629 | 3.610976867768595 | 76.09237844893885 | 7.844862571921487 | 33.04009656920238 | 46.1014217529465 | 21.170256110268816 | 17.46341094419858 | 6.789802777175969 | 4.6011146595437955 | 4.178924615759248 |
| user_002 | Jogging | 1.446034903688e12 | 1.68669 | 0.767005 | 1.8771 | -5.3433436363636355 | -2.4519013636363636 | -0.11207609090909067 | 2.8449231561 | 0.5882966700250001 | 3.52350441 | 2.637560281040985 | 7.330031668983031 | 7.030033636363635 | 3.2189063636363637 | 1.9891760909090908 | 7.687181198347106 | 3.4079735454545457 | 4.361865388429752 | 49.42137292840412 | 10.361358177858678 | 3.9568215206443713 | 62.03217093976252 | 12.505821022904898 | 21.370784418181128 | 7.876050465795818 | 3.5363570270696507 | 4.622854574630391 |
| user_002 | Jogging | 1.446034903802e12 | -8.81307 | -2.91174 | -1.61005 | -0.5846565454545452 | -0.9922438181818181 | -2.317236909090909 | 77.6702028249 | 8.4782298276 | 2.5922610025 | 9.420227898251719 | 6.299631258550062 | 8.228413454545455 | 1.919496181818182 | 0.7071869090909091 | 4.653226421487603 | 2.1057176611570245 | 1.7256828016528922 | 67.70678797894467 | 3.684465592014579 | 0.5001133243895537 | 30.638221487023472 | 5.665971506206171 | 4.285349532874221 | 5.535180348193134 | 2.38033012546709 | 2.0701085799721284 |
| user_002 | Jogging | 1.446034903891e12 | -14.79104 | -15.78737 | 2.56686 | -12.119069090909091 | -13.000438181818181 | 1.3022913636363636 | 218.77486428160003 | 249.24105151689997 | 6.5887702596 | 21.785423706187125 | 18.60925747454722 | 2.671970909090909 | 2.7869318181818183 | 1.2645686363636366 | 4.556316016528926 | 5.576081132231405 | 3.255326991735537 | 7.1394285390281 | 7.766988959194216 | 1.5991338360745873 | 23.245656366794588 | 43.7177504637925 | 17.94761159847474 | 4.821374945676242 | 6.61193999245248 | 4.236462155912022 |
| user_002 | Jogging | 1.446034904036e12 | -4.5212 | -3.86975 | -2.72134 | -8.691145454545454 | -7.99092909090909 | -1.2721353636363635 | 20.441249440000004 | 14.974965062499999 | 7.405691395600001 | 6.543844886463921 | 12.128840394525708 | 4.169945454545454 | 4.121179090909091 | 1.4492046363636366 | 3.656264380165289 | 2.8268364462809914 | 1.3494450743801654 | 17.388445093884293 | 16.984117099346278 | 2.1001940780578603 | 14.260647954712015 | 10.580754409606984 | 2.6915586961146896 | 3.776327310325737 | 3.2528071583798175 | 1.6405970547683821 |
| user_002 | Jogging | 1.446034904052e12 | -2.4519 | -2.4519 | -1.34181 | -7.266326363636364 | -6.377991818181818 | -1.7737833636363638 | 6.011813610000001 | 6.011813610000001 | 1.8004540760999999 | 3.7180749449278188 | 10.010471067635713 | 4.814426363636364 | 3.926091818181818 | 0.43197336363636385 | 3.708519669421487 | 3.345269421487602 | 1.1705110743801652 | 23.17870121087686 | 15.414196964794213 | 0.18660098689131424 | 14.723721494830494 | 13.114459041074072 | 2.413511653743653 | 3.8371501788215814 | 3.62138910379347 | 1.5535480854301398 |
| user_002 | Jogging | 1.446034904068e12 | -3.06503 | 1.03525 | -1.03525 | -6.00524 | -4.249472 | -1.9235813636363637 | 9.3944089009 | 1.0717425625 | 1.0717425625 | 3.3967475658193975 | 7.858612444492149 | 2.9402099999999995 | 5.284722 | 0.8883313636363637 | 3.514700991735537 | 4.132577867768595 | 1.2607692396694217 | 8.644834844099996 | 27.928286617284005 | 0.7891326116200413 | 13.18482379751194 | 17.94021551195684 | 2.50944098331362 | 3.6310912681330305 | 4.235589157597422 | 1.584121517849442 |
| user_002 | Jogging | 1.446034904141e12 | -4.98104 | -1.03405 | 0.459245 | -4.110125454545455 | 2.7248261818181816 | -2.1918236363636363 | 24.8107594816 | 1.0692594024999997 | 0.210905970025 | 5.107927647698722 | 6.009845601241079 | 0.8709145454545455 | 3.7588761818181817 | 2.6510686363636364 | 2.47118347107438 | 4.154430181818182 | 1.6769083719008264 | 0.7584921454842976 | 14.129150150240033 | 7.028164914710951 | 7.068442432986174 | 20.290694168959366 | 3.8194279934045157 | 2.658654252246082 | 4.504519304982427 | 1.9543356910736998 |
| user_002 | Jogging | 1.44603490419e12 | -0.842448 | -2.18366 | 1.30229 | -3.5527377272727274 | -0.22583909090909077 | -0.26884136363636374 | 0.7097186327039999 | 4.768370995600001 | 1.6959592440999998 | 2.6784415006499582 | 5.301884437122247 | 2.7102897272727273 | 1.9578209090909093 | 1.5711313636363637 | 2.3647737520661156 | 3.229358115702479 | 2.469601165289256 | 7.345670405760075 | 3.8330627120735548 | 2.46845376180186 | 6.257878597314694 | 12.208151199119698 | 6.884548005580183 | 2.5015752231973147 | 3.4940164852386855 | 2.623842221929547 |
| user_002 | Jogging | 1.44603490423e12 | -9.2346 | -13.33487 | -3.87095 | -2.5668612727272726 | -4.789440909090909 | -0.23748899999999998 | 85.27783716 | 177.8187579169 | 14.9842539025 | 16.675756324059186 | 5.879288130861806 | 6.667738727272727 | 8.545429090909092 | 3.633461 | 2.5617600909090905 | 4.247855041322314 | 2.637450396694215 | 44.45873973517253 | 73.0243583477554 | 13.202038838521 | 8.912440530990839 | 21.106037928951018 | 7.913980677297903 | 2.985371087652394 | 4.594130813217123 | 2.813179816026324 |
| user_002 | Jogging | 1.446034904247e12 | -11.4955 | -11.95534 | -4.94392 | -4.120576727272727 | -6.674103636363637 | -1.198980909090909 | 132.14652025 | 142.93015451559998 | 24.442344966400004 | 17.306617801638772 | 8.42209099464779 | 7.374923272727273 | 5.281236363636363 | 3.7449390909090914 | 3.597359198347107 | 4.569618677685951 | 2.868639413223141 | 54.389493278614346 | 27.89145752859503 | 14.024568794619011 | 18.159871432887243 | 25.35676770577446 | 9.372191101671767 | 4.26144006562186 | 5.035550387571796 | 3.061403452939806 |
| user_002 | Jogging | 1.446034904433e12 | -4.48288 | -1.80046 | 1.45557 | -9.56206 | -3.9951645454545455 | -3.5992239999999995 | 20.0962130944 | 3.2416562115999996 | 2.1186840249000003 | 5.045448773984332 | 11.195118393459259 | 5.079180000000001 | 2.1947045454545453 | 5.054793999999999 | 5.569430082644629 | 2.9224779338842977 | 3.0064033553719 | 25.79806947240001 | 4.816728041838842 | 25.550942382435995 | 33.99202304405725 | 8.910876950714426 | 11.493034828647646 | 5.830267836391159 | 2.9851092024772603 | 3.390137877527645 |
| user_002 | Jogging | 1.446034904538e12 | 2.72134 | -2.6435 | -3.41111 | 3.4424599090909087 | -0.6090423636363635 | -1.4950904545454546 | 7.405691395600001 | 6.98809225 | 11.635671432099999 | 5.101907004023103 | 4.55478708374157 | 0.7211199090909086 | 2.034457636363636 | 1.9160195454545452 | 4.419503983471075 | 1.9660537438016528 | 2.015458099173554 | 0.5200139232872804 | 4.139017874158314 | 3.6711308985638422 | 22.016640849837806 | 4.48710159313118 | 4.649995244198693 | 4.692189345053949 | 2.1182779782481758 | 2.1563847625594774 |
| user_002 | Jogging | 1.446034904684e12 | -12.22358 | -12.76007 | -0.767005 | -8.001378818181818 | -5.799704 | -0.22703663636363636 | 149.4159080164 | 162.81938640490003 | 0.5882966700250001 | 17.686819699746053 | 10.153678420593193 | 4.222201181818182 | 6.9603660000000005 | 0.5399683636363637 | 6.781428462809917 | 3.266728289256198 | 1.2351171652892563 | 17.826982819746853 | 48.446694853956004 | 0.2915658337281323 | 48.622979422365624 | 18.36825048902798 | 2.086747875931804 | 6.973017956549777 | 4.285819698614021 | 1.44455802096413 |
| user_002 | Jogging | 1.446034904692e12 | -12.6451 | -12.30022 | -0.920286 | -9.081314545454545 | -6.775130363636364 | -0.1573635454545455 | 159.89855400999997 | 151.29541204839998 | 0.8469263217960001 | 17.664679232304106 | 11.538779339376402 | 3.563785454545455 | 5.525089636363636 | 0.7629224545454545 | 6.725056115702478 | 3.691418768595042 | 1.289588834710744 | 12.700566766029754 | 30.526615489852855 | 0.5820506716496611 | 48.18622526434305 | 21.077174385491052 | 2.1372244049430615 | 6.94162987088357 | 4.59098838873407 | 1.4619248971623207 |
| user_002 | Jogging | 1.446034904756e12 | -14.06296 | -10.57581 | -0.690364 | -14.644726363636364 | -12.767033636363635 | -0.25839 | 197.76684396160002 | 111.84775715610002 | 0.476602452496 | 17.609406678539628 | 19.475279502295486 | 0.5817663636363637 | 2.1912236363636346 | 0.43197399999999997 | 3.2382232727272733 | 4.010647933884297 | 0.4766286280991736 | 0.33845210185867775 | 4.8014610245586695 | 0.18660153667599996 | 12.985092626615568 | 20.309954201626507 | 0.2758041644698933 | 3.603483401739984 | 4.506656654508585 | 0.5251706051083718 |
| user_002 | Jogging | 1.446034904846e12 | -5.86241 | 0.881966 | -2.22318 | -8.245237272727273 | -6.050527636363636 | -2.007189090909091 | 34.3678510081 | 0.7778640251560001 | 4.9425293124000005 | 6.331527805013257 | 10.729046543855477 | 2.3828272727272735 | 6.9324936363636365 | 0.21599090909090934 | 4.018248099173554 | 3.8266475206611585 | 0.9485068347107437 | 5.677865811652896 | 48.05946801822232 | 4.665207280991746e-2 | 18.196603209903987 | 16.717464723465596 | 1.2873520720054776 | 4.265747673023334 | 4.08869963722766 | 1.134615385055869 |
| user_002 | Jogging | 1.446034904854e12 | -5.36425 | 3.14286 | -2.52974 | -7.541537272727272 | -4.8382130909090915 | -2.0594445454545456 | 28.7751780625 | 9.877568979600001 | 6.3995844675999995 | 6.712103359581109 | 9.819530956042142 | 2.1772872727272716 | 7.981073090909092 | 0.4702954545454543 | 4.071453140495868 | 4.330194743801655 | 0.856348520661157 | 4.74057986798016 | 63.69752768243321 | 0.22117781456611546 | 18.397149987892792 | 21.966001243425268 | 1.1072440962802064 | 4.289189898791238 | 4.686790078873308 | 1.0522566684417858 |
| user_002 | Jogging | 1.446034904878e12 | -2.10702 | 4.5224 | -4.36911 | -5.378180909090909 | -1.4207367272727274 | -2.4983863636363637 | 4.439533280399999 | 20.45210176 | 19.0891221921 | 6.631798943914087 | 7.87933259704107 | 3.271160909090909 | 5.943136727272727 | 1.8707236363636364 | 3.951109338842975 | 5.189711123966943 | 0.8959354462809919 | 10.70049369316446 | 35.320874159057986 | 3.4996069236495866 | 17.659479898589257 | 31.220205179266724 | 1.239098651056581 | 4.20231839567033 | 5.587504378456157 | 1.1131480813694918 |
| user_002 | Jogging | 1.44603490491e12 | -1.34061 | -1.11069 | 0.497565 | -3.281014181818182 | 1.4567706363636361 | -2.52974 | 1.7972351721000002 | 1.2336322760999998 | 0.24757092922499999 | 1.810645845389153 | 5.179318167006664 | 1.940404181818182 | 2.567460636363636 | 3.0273049999999997 | 2.96301573553719 | 4.889799148760331 | 1.2677373636363638 | 3.7651683888174885 | 6.591854119276767 | 9.164575563024998 | 9.386070086848262 | 30.130454763090782 | 2.3786902039375906 | 3.0636693827579147 | 5.489121492833874 | 1.5423002962904437 |
| user_002 | Jogging | 1.446034904918e12 | -1.30229 | -2.03038 | 1.53221 | -2.9361314545454547 | 1.5821824545454546 | -2.1813736363636362 | 1.6959592440999998 | 4.1224429444 | 2.3476674841 | 2.857633579135016 | 4.84376388010314 | 1.6338414545454547 | 3.6125624545454547 | 3.7135836363636363 | 2.677355123966942 | 4.804924578512398 | 1.5457965702479337 | 2.669437898591207 | 13.05060748799148 | 13.790703424267768 | 7.55499995359014 | 29.43798477202156 | 3.5933962969675406 | 2.748636016934607 | 5.425678277600097 | 1.8956255687681416 |
| user_002 | Jogging | 1.446034905008e12 | -14.40784 | -15.86401 | -3.67935 | -4.430622363636363 | -7.21407090909091 | -0.7252006363636364 | 207.5858534656 | 251.6668132801 | 13.5376164225 | 21.74374124129056 | 8.775807580478014 | 9.977217636363637 | 8.64993909090909 | 2.9541493636363634 | 2.9516145867768597 | 5.136505157024794 | 1.7073115454545453 | 99.54487176336559 | 74.82144627643717 | 8.72699846267313 | 19.804817630125125 | 33.486343327562736 | 4.005089556027638 | 4.450260400260318 | 5.786738574323428 | 2.0012719845207543 |
| user_002 | Jogging | 1.446034905072e12 | -17.51178 | -12.83671 | -9.88724 | -16.44578272727273 | -14.568086363636365 | -6.950508181818182 | 306.6624387684001 | 164.7811236241 | 97.75751481760001 | 23.857935309035025 | 23.287859721835208 | 1.065997272727273 | 1.7313763636363646 | 2.9367318181818183 | 7.326463776859504 | 4.696928760330579 | 3.9678935537190085 | 1.1363501854619842 | 2.997664112558681 | 8.624393771921488 | 63.111678662534125 | 30.135037766228844 | 17.07495917870299 | 7.94428591268807 | 5.489538939312558 | 4.132185762850333 |
| user_002 | Jogging | 1.44603490508e12 | -17.24354 | -10.95901 | -8.69931 | -17.327149999999996 | -14.338164545454545 | -7.570600909090908 | 297.3396717316 | 120.09990018009998 | 75.67799447610001 | 22.20625061526146 | 23.891116942422705 | 8.360999999999663e-2 | 3.3791545454545453 | 1.1287090909090924 | 6.854269198347108 | 4.205415454545455 | 3.891886876033058 | 6.990632099999437e-3 | 11.418685442066115 | 1.27398421190083 | 60.58007336537378 | 24.155800296036443 | 16.839833156973718 | 7.7833202019044405 | 4.914855063583914 | 4.103636577107397 |
| user_002 | Jogging | 1.446034905177e12 | -5.47921 | 0.345482 | 2.2603 | -12.596334545454546 | -2.800267454545455 | -3.7943080000000005 | 30.021742224100002 | 0.119357812324 | 5.1089560899999995 | 5.937175770214656 | 13.939753347675527 | 7.117124545454546 | 3.145749454545455 | 6.054608 | 3.6648138842975198 | 5.536176719008265 | 3.1514500495867774 | 50.653461795511575 | 9.895739630773027 | 36.658278033664 | 18.033930212002403 | 31.976428244376894 | 13.719504742224348 | 4.246637518319924 | 5.654770397140532 | 3.703984981371327 |
| user_002 | Jogging | 1.446034905234e12 | 5.97857 | 1.83997 | -2.6447 | -1.852711727272727 | 1.1327883636363636 | 0.6821992727272729 | 35.7432992449 | 3.3854896009 | 6.994438089999999 | 6.791408317558296 | 6.010528689599741 | 7.831281727272727 | 0.7071816363636365 | 3.326899272727273 | 7.4420579008264465 | 2.783447090909091 | 4.073986148760331 | 61.32897349191571 | 0.5001058668099506 | 11.068258770873257 | 57.684432289683144 | 10.435126585578507 | 19.986831797446357 | 7.595026812966703 | 3.230344654302155 | 4.470663462781151 |
| user_002 | Jogging | 1.446034905339e12 | -5.47921 | -3.52487 | -4.59904 | -1.4451221818181816 | -3.3228180909090907 | -1.2372999999999998 | 30.021742224100002 | 12.424708516899999 | 21.151168921599997 | 7.97481157536653 | 6.509562618986421 | 4.034087818181819 | 0.2020519090909092 | 3.3617399999999997 | 5.088052165289255 | 2.322020347107438 | 1.70287826446281 | 16.273864524802946 | 4.0824973967281034e-2 | 11.301295827599999 | 31.004942615609924 | 7.3387421684781975 | 4.120292266177289 | 5.568208205123971 | 2.70901128983956 | 2.0298503063470688 |
| user_002 | Jogging | 1.446034905355e12 | -5.63249 | -2.95007 | -4.75232 | -3.6328649090909084 | -3.6398318181818183 | -2.097765272727273 | 31.724943600099998 | 8.702913004900001 | 22.5845453824 | 7.938035146520832 | 6.782794744382937 | 1.9996250909090914 | 0.6897618181818181 | 2.654554727272727 | 4.8840994710743795 | 2.0626460743801656 | 2.206426214876033 | 3.998500504193192 | 0.47577136582148755 | 7.0466608000859825 | 29.68185233968346 | 6.288701452326577 | 6.453050962520794 | 5.448105389920744 | 2.5077283450020214 | 2.540285606486167 |
| user_002 | Jogging | 1.446034905469e12 | -15.55745 | -16.05561 | 1.76214 | -14.630792727272727 | -14.028118181818183 | 3.1346995454545454 | 242.0342505025 | 257.7826124721 | 3.1051373796000004 | 22.425922508432066 | 21.330167876689657 | 0.9266572727272724 | 2.0274918181818187 | 1.3725595454545454 | 4.255771157024793 | 3.5739233057851227 | 5.2546326280991735 | 0.8586937010983465 | 4.110723072794217 | 1.8839197058183883 | 27.394483158568065 | 25.26332975107513 | 37.281628055188655 | 5.233973935602666 | 5.026263995362274 | 6.105868329336022 |
| user_002 | Jogging | 1.446034905615e12 | -3.67815 | 2.60638 | -1.64837 | -6.73681 | -4.211151818181819 | -2.275431818181818 | 13.5287874225 | 6.793216704400001 | 2.7171236568999997 | 4.7999091432859435 | 8.70367169629476 | 3.05866 | 6.81753181818182 | 0.6270618181818182 | 4.062901983471075 | 4.511978595041322 | 1.0020288842975207 | 9.3554009956 | 46.47874009192151 | 0.3932065238214876 | 17.284075030573106 | 22.3073486315281 | 1.8275789039897425 | 4.157412059271141 | 4.72306559678437 | 1.3518797668393971 |
| user_002 | Jogging | 1.446034905647e12 | -1.30229 | 3.25783 | -5.36544 | -3.6154460909090913 | -0.4418272727272725 | -2.857203636363636 | 1.6959592440999998 | 10.613456308899998 | 28.787946393600006 | 6.410722420024127 | 6.502811399641062 | 2.313156090909091 | 3.699657272727272 | 2.5082363636363643 | 3.985945512396695 | 4.648158429752066 | 0.9136700826446282 | 5.3506911009098275 | 13.687463935643796 | 6.291249655867772 | 16.803033608622233 | 24.367466543648614 | 1.6582827814492298 | 4.099150352039095 | 4.936341412792334 | 1.2877432901977124 |
| user_002 | Jogging | 1.446034905727e12 | 1.07357 | -6.70546 | 1.53221 | -1.8213575454545456 | -2.3613268181818183 | 0.4035049090909093 | 1.1525525448999998 | 44.96319381160001 | 2.3476674841 | 6.96156690987022 | 5.193493556753845 | 2.8949275454545456 | 4.344133181818182 | 1.1287050909090908 | 1.1024227851239672 | 3.1625355371900823 | 3.0599230743801655 | 8.38060549343148 | 18.871493101373762 | 1.2739751822440988 | 1.9395304452291355 | 11.662849426100301 | 12.069842032723182 | 1.3926702571783227 | 3.4150914228026608 | 3.474167818733456 |
| user_002 | Jogging | 1.446034905857e12 | -17.20522 | -5.82409 | -6.20849 | -18.59172 | -10.513101818181818 | -8.033929090909092 | 296.01959524840004 | 33.9200243281 | 38.545348080100005 | 19.195962274827487 | 22.924337680223914 | 1.386499999999998 | 4.689011818181818 | 1.825439090909092 | 6.054610214876033 | 2.2665984297520656 | 3.3813718842975207 | 1.9223822499999947 | 21.98683183104876 | 3.3322278746190124 | 54.52274721195797 | 7.463359831681368 | 14.768475546288306 | 7.383952004987435 | 2.7319150484012797 | 3.842977432445877 |
| user_002 | Jogging | 1.446034905874e12 | -14.21624 | -3.90807 | -6.55337 | -18.072653636363636 | -8.962870909090908 | -8.103602727272728 | 202.10147973760002 | 15.2730111249 | 42.9466583569 | 16.134470837910985 | 21.862878092852522 | 3.8564136363636354 | 5.054800909090908 | 1.5502327272727277 | 4.436605834710743 | 2.4822690909090905 | 2.7169417520661163 | 14.871926134731398 | 25.551012230546267 | 2.403221508707439 | 30.28166977085812 | 9.45894439590338 | 10.4348205853482 | 5.50287831692271 | 3.0755396918107527 | 3.230297290552094 |
| user_002 | Jogging | 1.446034906043e12 | 4.79064 | -6.43721 | -3.52607 | 5.079782727272728 | -0.9121207272727273 | -0.6102407272727273 | 22.9502316096 | 41.43767258410001 | 12.4331696449 | 8.764763193526681 | 6.032297508238341 | 0.2891427272727283 | 5.525089272727273 | 2.9158292727272723 | 4.460991074380165 | 2.1611383471074377 | 1.8317732727272726 | 8.360351673471132e-2 | 30.52661147160599 | 8.502060347693254 | 26.557241211071098 | 7.379857479956052 | 4.511605649858295 | 5.1533718293046835 | 2.7165893101379996 | 2.1240540600131377 |
| user_002 | Jogging | 1.4460349061e12 | -8.69811 | -6.62882 | -5.67201 | -2.187142090909091 | -5.298055454545454 | -2.2510471818181816 | 75.65711757209999 | 43.9412545924 | 32.171697440100004 | 12.31949956794512 | 9.109836937026863 | 6.510967909090908 | 1.3307645454545458 | 3.4209628181818186 | 5.894360165289256 | 3.051057355371901 | 1.7427830826446282 | 42.39270311321163 | 1.770934275438844 | 11.70298660338249 | 49.28009459437685 | 11.136380565774072 | 4.236912042945018 | 7.019978247429036 | 3.337121598889389 | 2.0583760693675535 |
| user_002 | Jogging | 1.446034906132e12 | -15.74905 | -17.81835 | 0.344284 | -8.572700272727273 | -9.20672818181818 | -1.6274719090909096 | 248.0325759025 | 317.49359672249994 | 0.11853147265599999 | 23.78328623419514 | 13.337573546264622 | 7.1763497272727275 | 8.611621818181819 | 1.9717559090909096 | 7.523449586776858 | 4.22220267768595 | 1.909365611570248 | 51.49999540812735 | 74.16003033938513 | 3.8878213650349194 | 61.7491867891929 | 25.090541422510398 | 5.45840222276125 | 7.858065079215932 | 5.009045959313051 | 2.336322371326622 |
| user_002 | Jogging | 1.446034906319e12 | -3.56319 | -4.98104 | -1.22685 | -7.67043181818182 | -8.49606 | -1.3487758181818184 | 12.696322976100001 | 24.8107594816 | 1.5051609225 | 6.2459781764108016 | 11.641939368073643 | 4.107241818181819 | 3.51502 | 0.12192581818181836 | 5.061765454545454 | 2.8714898347107436 | 0.936471603305785 | 16.869435353021498 | 12.355365600399999 | 1.4865905139305828e-2 | 26.613912440978808 | 10.115667026524044 | 1.3641360147597592 | 5.158867360281596 | 3.1805136419333344 | 1.1679623344781969 |
| user_002 | Jogging | 1.446034906366e12 | -3.60151 | 4.33079 | -5.67201 | -4.806859090909091 | -2.3578414545454547 | -2.4461315454545454 | 12.970874280100002 | 18.7557420241 | 32.171697440100004 | 7.993642082574126 | 7.147612929840088 | 1.205349090909091 | 6.688631454545455 | 3.225878454545455 | 2.907910165289256 | 5.069999818181818 | 0.8607821404958678 | 1.452866430955372 | 44.737790734734844 | 10.406291803500572 | 11.57677391751818 | 27.53574990156487 | 1.5309432143551114 | 3.402465858391261 | 5.247451753143126 | 1.237312900747063 |
| user_002 | Jogging | 1.446034906408e12 | -3.37159 | -2.6435 | -0.537083 | -3.883689090909091 | 0.8924172727272727 | -3.6201266363636364 | 11.3676191281 | 6.98809225 | 0.28845814888899995 | 4.3178894760043365 | 6.298627808131697 | 0.512099090909091 | 3.5359172727272727 | 3.0830436363636364 | 1.0894361157024794 | 4.591786280991736 | 1.653474876033058 | 0.26224547890991745 | 12.502710959571074 | 9.505158063722314 | 1.5332669035144262 | 25.43087279685827 | 3.9924662865406377 | 1.2382515509840584 | 5.042903211133273 | 1.9981156839734375 |
| user_002 | Jogging | 1.446034906432e12 | -0.919089 | -6.01569 | 1.22565 | -3.0406417272727277 | -1.6819454545454447e-2 | -2.571543909090909 | 0.8447245899210001 | 36.188526176100005 | 1.5022179224999999 | 6.207694313392132 | 6.2486913175856165 | 2.1215527272727277 | 5.998870545454546 | 3.797193909090909 | 1.1369413223140497 | 4.950603520661157 | 2.6136990661157027 | 4.500985974598349 | 35.98644782112212 | 14.418681583237097 | 1.6017300063233662 | 28.800501000828568 | 8.60560462482137 | 1.2655947243582228 | 5.366609823792723 | 2.9335310846864004 |
| user_002 | Jogging | 1.44603490644e12 | -0.535886 | -4.44456 | 1.14901 | -2.6713731818181823 | -0.6369121818181819 | -2.2440793636363634 | 0.287173804996 | 19.7541135936 | 1.3202239801000002 | 4.621851509806 | 6.148168785920205 | 2.135487181818182 | 3.807647818181818 | 3.3930893636363635 | 1.2534859421487603 | 4.643724429752066 | 2.887324933884298 | 4.560305503709762 | 14.49818190730476 | 11.513055429622222 | 1.950079957320572 | 25.427603458128235 | 9.638896397270868 | 1.3964526333966978 | 5.0425790482776005 | 3.1046572109124813 |
| user_002 | Jogging | 1.446034906586e12 | -18.73803 | -8.77475 | -10.577 | -17.306248181818184 | -11.910050909090911 | -9.437845454545455 | 351.11376828089993 | 76.99623756249999 | 111.872929 | 23.237532890636217 | 23.287164041305836 | 1.4317818181818147 | 3.135300909090912 | 1.139154545454545 | 7.446174330578511 | 3.08209305785124 | 5.358825710743801 | 2.049999174876023 | 9.8301117905463 | 1.2976730784297512 | 71.52977110671443 | 15.74948846692201 | 31.739869182092267 | 8.457527481877575 | 3.968562518963511 | 5.633814798348652 |
| user_002 | Jogging | 1.44603490665e12 | -11.84038 | -1.8771 | -5.05888 | -16.773248181818182 | -5.029813636363637 | -8.096633636363636 | 140.19459854439998 | 3.52350441 | 25.592266854400002 | 13.011931824629269 | 19.655628983671996 | 4.932868181818183 | 3.152713636363637 | 3.037753636363636 | 1.9451499999999997 | 4.46225694214876 | 2.7549444214876035 | 24.333188499194225 | 9.939603272913228 | 9.227947155240493 | 5.12739384022141 | 23.162048677438687 | 8.607126122767712 | 2.2643749336674373 | 4.812696611821556 | 2.933790401983024 |
| user_002 | Jogging | 1.446034906675e12 | -7.28026 | -0.612526 | -1.95493 | -14.35906818181818 | -2.295136727272727 | -5.773029999999999 | 53.0021856676 | 0.375188100676 | 3.8217513049000003 | 7.563010318198436 | 15.684182358959674 | 7.0788081818181805 | 1.6826107272727273 | 3.8180999999999994 | 3.3383008264462815 | 4.440089074380165 | 2.8638885950413226 | 50.109525274976015 | 2.8311788595332565 | 14.577887609999996 | 16.27907204168686 | 22.948845464150352 | 8.696309456470926 | 4.034733205763035 | 4.790495325553544 | 2.9489505686720023 |
| user_002 | Jogging | 1.446034906691e12 | -3.56319 | -0.267643 | -0.575403 | -11.673162727272727 | -1.3859 | -4.44575390909091 | 12.696322976100001 | 7.163277544900001e-2 | 0.331088612409 | 3.6192601956695514 | 12.589850897609228 | 8.109972727272726 | 1.1182569999999998 | 3.87035090909091 | 4.516729669421488 | 3.7012356942148754 | 3.247091570247935 | 65.77165763710742 | 1.2504987180489997 | 14.979616159500832 | 26.88297084850962 | 17.91654209487913 | 10.707331798979867 | 5.184879058233626 | 4.232793651346488 | 3.2722059530200522 |
| user_002 | Jogging | 1.446034906796e12 | 3.48775 | -1.8771 | -0.15388 | 4.167061818181818 | -0.2641598181818182 | -1.0526639999999998 | 12.1644000625 | 3.52350441 | 2.3679054399999996e-2 | 3.9637839909485484 | 4.577401749353625 | 0.6793118181818176 | 1.6129401818181819 | 0.8987839999999998 | 4.622822 | 1.3089080247933884 | 0.7635545371900826 | 0.46146454632148687 | 2.6015760301236694 | 0.8078126786559997 | 29.624850336280137 | 2.1701642934302128 | 0.9407629565015231 | 5.4428715156872975 | 1.473147750033992 | 0.9699293564489752 |
| user_002 | Jogging | 1.446034906901e12 | -13.90968 | -14.82936 | 0.574206 | -9.422715454545456 | -7.56243818181818 | -1.536894909090909 | 193.4791977024 | 219.90991800959998 | 0.329712530436 | 20.34007935683723 | 12.62745717317743 | 4.486964545454544 | 7.266921818181819 | 2.1111009090909088 | 5.990321280991736 | 3.6831837438016533 | 1.5651153305785124 | 20.132850832166103 | 52.808152711566954 | 4.456747048364462 | 39.355071607140815 | 25.29494812891069 | 3.5116251320448835 | 6.27336206568223 | 5.029408327915988 | 1.8739330649852153 |
| user_002 | Jogging | 1.446034906942e12 | -13.25823 | -11.95534 | 0.957409 | -12.234032727272727 | -11.370082727272724 | 6.907418181818191e-2 | 175.78066273289997 | 142.93015451559998 | 0.9166319932809999 | 17.878127677186473 | 17.14557775538888 | 1.0241972727272728 | 0.5852572727272758 | 0.888334818181818 | 3.535603636363635 | 4.105973884297521 | 2.7207424297520664 | 1.0489800534619835 | 0.3425260752801689 | 0.7891387491941237 | 14.143379773647181 | 27.92088436341037 | 9.16121299243646 | 3.7607685083832507 | 5.284021608908348 | 3.0267495754416913 |
| user_002 | Jogging | 1.446034906982e12 | -18.20155 | -13.83303 | 0.114362 | -14.93735727272727 | -13.091012727272728 | 0.9992131818181815 | 331.2964224025 | 191.35271898090002 | 1.3078667044000002e-2 | 22.861807016297817 | 20.021431218737533 | 3.2641927272727305 | 0.7420172727272725 | 0.8848511818181816 | 2.679888842975208 | 2.0886154545454545 | 2.392960826446281 | 10.654954160780186 | 0.5505896330256195 | 0.7829616139650326 | 8.170243855914727 | 9.0530824903009 | 7.778509837779979 | 2.8583638424656033 | 3.008834074903583 | 2.7889979988841835 |
| user_002 | Jogging | 1.446034907039e12 | -10.49917 | -9.08132 | -1.49509 | -15.240435454545455 | -12.43260181818182 | -0.7321686363636364 | 110.23257068889998 | 82.47037294239999 | 2.2352941081 | 13.962028424960321 | 19.741622779485223 | 4.741265454545456 | 3.3512818181818194 | 0.7629213636363636 | 2.6935066115702493 | 1.4317850413223132 | 1.1613272644628099 | 22.479598110466128 | 11.23108982487604 | 0.5820490070927686 | 8.706909684042682 | 2.7814735594394424 | 2.090464572249903 | 2.9507473094188668 | 1.6677750326226384 | 1.4458438962245899 |
| user_002 | Jogging | 1.44603490712e12 | -3.06503 | 0.11556 | -2.33814 | -5.9286 | -3.9533609090909083 | -2.0037054545454542 | 9.3944089009 | 1.3354113599999998e-2 | 5.466898659600001 | 3.8567682940643455 | 7.647626963046448 | 2.86357 | 4.068920909090909 | 0.3344345454545459 | 4.9176669421487595 | 4.3891014049586765 | 0.6926151570247933 | 8.2000331449 | 16.556117364437185 | 0.11184646519338871 | 24.717201992823444 | 19.88589517058234 | 0.711008412292816 | 4.971639769012176 | 4.459360399270543 | 0.8432131476043385 |
| user_002 | Jogging | 1.446034907209e12 | -2.60518 | -2.91174 | -0.268841 | -3.305399090909091 | 1.7563679090909092 | -2.2754321818181817 | 6.7869628323999995 | 8.4782298276 | 7.2275483281e-2 | 3.9163079734976156 | 5.2126819531418045 | 0.7002190909090911 | 4.668107909090909 | 2.0065911818181816 | 0.514316033057851 | 3.7867450082644627 | 1.3510296363636363 | 0.49030677527355393 | 21.7912314509171 | 4.026408170950487 | 0.3872811612404208 | 17.10425060984232 | 2.3858715021441905 | 0.6223191795537245 | 4.135728546440436 | 1.5446266546140497 |
| user_002 | Jogging | 1.446034907257e12 | -4.9044 | -8.73643 | -1.03525 | -3.1416672727272728 | -3.047608181818182 | -0.3977359999999999 | 24.05313936 | 76.3252091449 | 1.0717425625 | 10.072243596508178 | 5.088328791502168 | 1.7627327272727271 | 5.688821818181818 | 0.6375140000000001 | 0.4753612396694214 | 3.6527827355371896 | 1.6974960330578512 | 3.1072266677983467 | 32.36269367902149 | 0.40642410019600017 | 0.44791110553456037 | 15.898295291524887 | 4.0854805384043535 | 0.6692616121776 | 3.9872666441467 | 2.0212571678053126 |
| user_002 | Jogging | 1.44603490729e12 | -14.5228 | -14.98264 | -4.714 | -6.297867272727273 | -8.168595454545455 | -1.5194766363636367 | 210.91171984000002 | 224.4795013696 | 22.221796000000005 | 21.39189138925308 | 10.659201066417712 | 8.224932727272726 | 6.814044545454545 | 3.194523363636364 | 2.833486611570248 | 5.709093859504131 | 2.4113304049586772 | 67.64951836816196 | 46.43120306743883 | 10.204979520818588 | 18.223977300926823 | 37.259208245871726 | 7.400173065667297 | 4.268955059604964 | 6.104032130147393 | 2.720325911663398 |
| user_002 | Jogging | 1.446034907403e12 | -16.32385 | -2.98839 | -6.70665 | -18.60913818181818 | -8.074537272727271 | -9.162636363636363 | 266.4680788225 | 8.9304747921 | 44.9791542225 | 17.899097961548232 | 22.465247190479637 | 2.2852881818181814 | 5.086147272727271 | 2.455986363636363 | 2.8151174380165287 | 3.4934809917355367 | 3.2850951074380172 | 5.222542073957849 | 25.868894079871062 | 6.0318690183677655 | 13.08808219941119 | 15.12350955082885 | 15.124677542114034 | 3.6177454580734656 | 3.888895672402237 | 3.8890458395490834 |
| user_002 | Jogging | 1.446034907476e12 | -1.72382 | -1.8771 | 7.6042e-2 | -9.690955454545456 | -2.1314045454545454 | -3.644511272727273 | 2.9715553923999996 | 3.52350441 | 5.782385764e-3 | 2.5496749181344667 | 10.660497360124252 | 7.967135454545456 | 0.2543045454545454 | 3.720553272727273 | 5.511791239669422 | 2.9132938842975205 | 2.9712496363636363 | 63.47524735107523 | 6.467080183884294e-2 | 13.842516655201623 | 34.147024354188275 | 11.661232288066863 | 9.041529347618932 | 5.843545529401502 | 3.4148546510893936 | 3.0069135916449166 |
| user_002 | Jogging | 1.446034907508e12 | 3.90927 | -1.18733 | 0.689167 | -3.514419181818181 | -1.7725872727272725 | -0.8575789090909095 | 15.282391932899998 | 1.4097525289 | 0.47495115388899994 | 4.143319395809234 | 5.937236157961707 | 7.423689181818181 | 0.5852572727272725 | 1.5467459090909095 | 7.149745727272728 | 1.24398520661157 | 3.22270714876033 | 55.1111610682443 | 0.342526075280165 | 2.392422907289464 | 51.78529644795792 | 2.918500147336363 | 10.751469564364557 | 7.196200139515153 | 1.708361831503023 | 3.278943360957087 |
| user_002 | Jogging | 1.44603490771e12 | -17.43514 | -8.54483 | 3.48655 | -12.686907272727273 | -8.206916181818181 | 0.26067618181818175 | 303.9841068196 | 73.01411972889998 | 12.1560309025 | 19.726993117325307 | 16.107928203941583 | 4.748232727272727 | 0.33791381818181776 | 3.2258738181818183 | 5.759764016528926 | 4.008115272727272 | 3.1717190578512398 | 22.5457140323438 | 0.11418574851821459 | 10.406261890830942 | 35.661160756290485 | 30.533416247583013 | 12.024295610812407 | 5.971696639673727 | 5.525705045293588 | 3.4676066113116706 |
| user_002 | Jogging | 1.446034907759e12 | -15.86401 | -13.71807 | 0.919089 | -16.041677272727274 | -11.666194545454545 | 0.6682655454545454 | 251.6668132801 | 188.18544452490002 | 0.8447245899210001 | 20.992784055358666 | 20.2523659330661 | 0.17766727272727323 | 2.0518754545454563 | 0.2508234545454546 | 3.3924554545454555 | 2.6558187107438016 | 2.6403015702479338 | 3.156565979834729e-2 | 4.210192880966122 | 6.291240535011575e-2 | 22.638652582823227 | 12.02031983930934 | 9.005259334218808 | 4.758009308820573 | 3.4670332907702717 | 3.000876427682221 |
| user_002 | Jogging | 1.446034907783e12 | -15.05928 | -13.10495 | -0.767005 | -15.623637272727272 | -12.209646363636363 | -3.1951000000000035e-2 | 226.78191411839998 | 171.73971450250002 | 0.5882966700250001 | 19.977735739841116 | 20.14726044376983 | 0.564357272727273 | 0.8953036363636375 | 0.735054 | 1.4406541322314053 | 1.6569572396694214 | 1.9061984958677685 | 0.3184991312801656 | 0.8015686012859525 | 0.540304382916 | 6.287423676600526 | 3.4110413267804827 | 6.197944188322239 | 2.5074735644868773 | 1.8469004647734764 | 2.489567068452312 |
| user_002 | Jogging | 1.446034907864e12 | -6.20729 | -2.56686 | -1.38013 | -12.718260000000003 | -7.496248181818183 | -2.3799413636363638 | 38.530449144100004 | 6.5887702596 | 1.9047588169000003 | 6.857403168882518 | 15.077684317245383 | 6.510970000000002 | 4.929388181818183 | 0.9998113636363637 | 2.894924958677685 | 3.608126859504132 | 1.1971138512396695 | 42.39273034090003 | 24.298867847048772 | 0.9996227628564051 | 11.904642383664612 | 15.582626079113071 | 2.082914951146491 | 3.4503104764157984 | 3.947483512202815 | 1.443230733855987 |
| user_002 | Jogging | 1.446034907913e12 | -3.63983 | 4.56072 | -1.34181 | -6.733325454545454 | -1.8318086363636361 | -2.6168318181818178 | 13.248362428899998 | 20.8001669184 | 1.8004540760999999 | 5.987402059608157 | 8.085788948485714 | 3.0934954545454545 | 6.392528636363636 | 1.2750218181818178 | 4.686161570247934 | 4.656393636363637 | 1.0932376942148758 | 9.569714127293388 | 40.86442236672913 | 1.6256806368396686 | 23.52487566892179 | 22.36712104831754 | 1.8064930810530717 | 4.850244908138329 | 4.729389077705231 | 1.3440584366213664 |
| user_002 | Jogging | 1.446034907945e12 | -3.06503 | 1.49509 | -3.25783 | -4.030002727272727 | 1.2512349999999997 | -2.5645781818181814 | 9.3944089009 | 2.2352941081 | 10.613456308899998 | 4.7162653994341746 | 5.7354608954132 | 0.9649727272727269 | 0.24385500000000038 | 0.6932518181818184 | 3.8944193388429755 | 4.135427107438016 | 0.7736911983471072 | 0.9311723643801645 | 5.946526102500018e-2 | 0.48059808341239696 | 18.571364015563038 | 19.700650420240237 | 0.7393206976024979 | 4.309450546828799 | 4.438541474430563 | 0.8598375995515071 |
| user_002 | Jogging | 1.446034908139e12 | -16.20889 | -13.83303 | -8.43107 | -5.102970909090908 | -0.7588395454545455 | -2.811918363636364 | 262.7281150321 | 191.35271898090002 | 71.08294134490001 | 22.91645206741 | 7.7895724282934165 | 11.105919090909092 | 13.074190454545455 | 5.619151636363636 | 3.8196790082644627 | 5.613133347107438 | 1.4023345041322315 | 123.34143885381903 | 170.9344560417275 | 31.57486511244813 | 22.279454978312547 | 45.07532239502894 | 3.9238172475664856 | 4.720111754854174 | 6.713815785008473 | 1.9808627533391823 |
| user_002 | Jogging | 1.446034908269e12 | -3.67815 | -1.76214 | 1.49389 | -5.963435454545455 | -1.3824163636363638 | -2.7317938181818175 | 13.5287874225 | 3.1051373796000004 | 2.2317073320999996 | 4.343458545237885 | 8.741728257991952 | 2.285285454545455 | 0.37972363636363626 | 4.2256838181818175 | 3.8149286776859497 | 5.324939834710744 | 1.9438858264462808 | 5.222529608757027 | 0.14419004001322305 | 17.856403731243663 | 23.576699133056877 | 43.9105411464654 | 6.288745057720135 | 4.855584324574837 | 6.626502934917134 | 2.5077370391889446 |
| user_002 | Jogging | 1.446034909046e12 | -5.05768 | -1.07237 | -0.652044 | -9.196275454545455 | -6.566109090909092 | -2.5088374545454544 | 25.580126982400003 | 1.1499774169 | 0.42516137793599995 | 5.211071461536101 | 12.418203811353674 | 4.138595454545455 | 5.4937390909090915 | 1.8567934545454543 | 4.934770495867768 | 5.036428710743802 | 2.7267596363636364 | 17.127972336384303 | 30.18116919898265 | 3.447681932842842 | 33.161018213630584 | 33.540879014491985 | 12.511976010260918 | 5.758560428929315 | 5.791448783723464 | 3.5372271640737067 |
| user_002 | Jogging | 1.446034910211e12 | -17.05194 | -11.6871 | -1.07357 | -8.475157272727273 | -6.458115909090908 | -2.310269909090909 | 290.76865776359995 | 136.58830640999997 | 1.1525525448999998 | 20.700471412953377 | 11.878641916477324 | 8.576782727272725 | 5.228984090909091 | 1.2366999090909092 | 6.322850239669421 | 4.93666985123967 | 2.176657570247934 | 73.56120195084377 | 27.342274622980376 | 1.529426665145463 | 49.513870967071085 | 29.244915742785835 | 8.943415354326099 | 7.036609337391915 | 5.407856853022816 | 2.9905543556882725 |
| user_002 | Jogging | 1.446034910923e12 | -15.0976 | -14.3312 | 1.49389 | -7.0747254545454545 | -5.541911818181819 | -2.4983859090909095 | 227.93752576 | 205.38329344000002 | 2.2317073320999996 | 20.869895220918096 | 10.728633853208215 | 8.022874545454545 | 8.789288181818183 | 3.9922759090909095 | 5.452567685950413 | 4.862880545454545 | 2.2213097768595045 | 64.36651597210248 | 77.25158674304878 | 15.938266934307647 | 42.44141270557199 | 30.712884458420195 | 8.367651130529412 | 6.514707415193103 | 5.541920647069948 | 2.892689255784211 |
| user_002 | Jogging | 1.446034911246e12 | -4.75112 | -6.39889 | 0.880768 | -6.914476363636364 | -5.099486363636363 | -1.561280909090909 | 22.573141254400003 | 40.945793232099994 | 0.775752269824 | 8.01839676969929 | 10.070073140367551 | 2.163356363636364 | 1.2994036363636363 | 2.442048909090909 | 5.080449173553718 | 4.724800603305787 | 1.8662933884297523 | 4.680110756085952 | 1.6884498101950411 | 5.963602874392099 | 38.99996173856371 | 29.48726106588099 | 4.7361626274450375 | 6.24499493503107 | 5.430217405029102 | 2.1762726454755246 |
| user_002 | Jogging | 1.446034912153e12 | -17.93331 | -10.53749 | -8.23947 | -8.192980727272726 | -5.761382454545454 | -2.0733795454545456 | 321.6036075561 | 111.0386955001 | 67.88886588090001 | 22.372553920755227 | 11.34494337446847 | 9.740329272727273 | 4.776107545454546 | 6.166090454545455 | 6.011221520661156 | 4.700413702479339 | 1.9571871735537187 | 94.8740143411478 | 22.811203285747848 | 38.02067149363658 | 47.0317002322874 | 26.73019534190938 | 7.5335368870795465 | 6.857966187747458 | 5.170125273328431 | 2.744728927795885 |
| user_002 | Jogging | 1.44603491306e12 | -0.957409 | 1.91661 | 1.22565 | -9.095248999999999 | -6.325737 | -2.470517545454545 | 0.9166319932809999 | 3.6733938920999996 | 1.5022179224999999 | 2.468247112402038 | 12.124781946634904 | 8.137839999999999 | 8.242347 | 3.696167545454545 | 4.828992140495868 | 4.8625619173553725 | 2.2665985950413226 | 66.22443986559998 | 67.93628406840901 | 13.661654524071476 | 33.47302909714301 | 31.912813317328006 | 7.9159821938666335 | 5.785588051109672 | 5.649142706404929 | 2.8135355327179776 |
| user_002 | Jogging | 1.446034913319e12 | -17.85667 | -13.41151 | -1.80165 | -8.078019 | -5.792736090909091 | -2.1569866363636367 | 318.86066348890006 | 179.86860048009999 | 3.2459427224999997 | 22.404803205819505 | 11.181984622796081 | 9.778651000000002 | 7.618773909090909 | 0.35533663636363677 | 5.500073561983472 | 5.078866123966942 | 2.0981160330578517 | 95.62201537980104 | 58.04571587784437 | 0.12626412514222343 | 41.78944792661123 | 32.60139135892502 | 6.684421911083967 | 6.464475843145462 | 5.7097628110916325 | 2.5854248995250213 |
| user_002 | Jogging | 1.446034914095e12 | -17.39682 | -10.49917 | -3.56439 | -8.861844909090909 | -5.841506090909092 | -1.6866936363636365 | 302.64934611240005 | 110.23257068889998 | 12.7048760721 | 20.629755036679423 | 11.92481561399505 | 8.534975090909093 | 4.657663909090908 | 1.8776963636363635 | 5.914944851239668 | 4.972139173553718 | 2.1484710495867767 | 72.84579980243868 | 21.693833090047995 | 3.5257436340132227 | 46.87801609592091 | 27.69571628107252 | 7.233094173347625 | 6.846752229774414 | 5.262671971638791 | 2.6894412381287727 |
| user_002 | Jogging | 1.446034914937e12 | -14.06296 | -9.31124 | 0.344284 | -10.049774545454547 | -6.2351611818181825 | -0.21658654545454545 | 197.76684396160002 | 86.6991903376 | 0.11853147265599999 | 16.86963442911126 | 12.252088776393206 | 4.013185454545454 | 3.0760788181818173 | 0.5608705454545454 | 5.10451879338843 | 4.026167958677686 | 2.1858417190082644 | 16.1056574925752 | 9.462260895666846 | 0.31457576875847926 | 34.7920575256337 | 20.439095374185214 | 5.985408657440974 | 5.898479255336388 | 4.520961775351038 | 2.446509484437159 |
| user_002 | Jogging | 1.446034915132e12 | -3.79311 | -0.650847 | 0.267643 | -8.910616363636363 | -4.698865454545454 | -0.1364618181818182 | 14.387683472099999 | 0.42360181740899994 | 7.163277544900001e-2 | 3.8578385224057783 | 10.584969232379587 | 5.1175063636363625 | 4.0480184545454545 | 0.4041048181818182 | 4.847044462809918 | 4.361232578512396 | 1.7323318264462806 | 26.188871381858664 | 16.38645340834057 | 0.16330070407776034 | 29.31876509625081 | 22.243799336105926 | 4.342268189926172 | 5.4146805165448875 | 4.716333251171499 | 2.083810977494401 |
| user_002 | Jogging | 1.446034915455e12 | 1.76333 | -1.72382 | -0.345482 | -10.067193636363639 | -5.4374033636363635 | -1.052664909090909 | 3.1093326889000004 | 2.9715553923999996 | 0.119357812324 | 2.4900292957360963 | 12.23813765690058 | 11.830523636363639 | 3.7135833636363635 | 0.707182909090909 | 5.288836694214875 | 4.095206685950413 | 1.8130885867768596 | 139.96128951055874 | 13.790701398676768 | 0.5001076669102809 | 38.75524016966116 | 20.051422205409718 | 7.297770799175755 | 6.22537068532157 | 4.4778814416428805 | 2.701438653602142 |
| user_002 | Jumping | 1.446035033798e12 | -1.30229 | 0.307161 | -0.920286 | -6.6575549999999994 | -7.12697975 | 0.7179085000000001 | 1.6959592440999998 | 9.434787992100001e-2 | 0.8469263217960001 | 1.6239561095722383 | 10.874421038711667 | 5.355264999999999 | 7.43414075 | 1.6381945 | 3.794508333333333 | 5.764013104166667 | 1.3292357083333333 | 28.678863220224994 | 55.26644869081056 | 2.68368121983025 | 20.47484638194861 | 50.37997567727834 | 2.93820038376659 | 4.524913964038278 | 7.0978852961483065 | 1.7141179608669266 |
| user_002 | Jumping | 1.446035033863e12 | -0.344284 | -0.535886 | 0.344284 | -5.394900799999999 | -5.8087610000000005 | 0.6431836000000001 | 0.11853147265599999 | 0.287173804996 | 0.11853147265599999 | 0.7240419534170655 | 8.844345221652747 | 5.050616799999999 | 5.272875000000001 | 0.2988996000000001 | 4.045730026666666 | 5.665785483333333 | 1.1231684866666667 | 25.508730060442232 | 27.80321076562501 | 8.934097088016006e-2 | 21.481623117647334 | 45.86462269494767 | 2.368428501189304 | 4.634827193935857 | 6.7723424821067395 | 1.5389699481111723 |
| user_002 | Jumping | 1.446035033927e12 | -0.842448 | -0.650847 | -1.26517 | -4.636158666666666 | -4.949108666666667 | 0.32512466666666673 | 0.7097186327039999 | 0.42360181740899994 | 1.6006551288999997 | 1.6534737914502906 | 7.645866649952336 | 3.793710666666666 | 4.298261666666667 | 1.5902946666666666 | 4.0037268 | 5.437864847222222 | 1.20102285 | 14.392240622380438 | 18.47505335513611 | 2.5290371268284444 | 20.300059368436184 | 41.29969447164574 | 2.3951966054624942 | 4.505558718786848 | 6.42648383423204 | 1.5476422730923622 |
| user_002 | Jumping | 1.446035034057e12 | -6.70546 | -11.6871 | 0.842448 | -4.46850775 | -5.37382525 | 0.20537325000000006 | 44.96319381160001 | 136.58830640999997 | 0.7097186327039999 | 13.50041550672808 | 7.712791209417686 | 2.2369522500000008 | 6.313274749999999 | 0.6370747499999999 | 3.647825774107143 | 5.225442836309524 | 1.1384730526785716 | 5.0039553687800655 | 39.85743806898755 | 0.40586423708756236 | 16.918744297309093 | 36.98160318013506 | 2.0470234572839994 | 4.11324012152331 | 6.081250133001854 | 1.4307422749342382 |
| user_002 | Jumping | 1.446035034445e12 | -1.76214 | -0.267643 | -0.996927 | -4.765055181818181 | -5.541912 | 0.2571926363636364 | 3.1051373796000004 | 7.163277544900001e-2 | 0.993863443329 | 2.042212917004003 | 7.982462080994015 | 3.002915181818181 | 5.274269 | 1.2541196363636364 | 4.781736989249638 | 6.35182557994228 | 1.1887342204971796 | 9.01749958919412 | 27.817913484361004 | 1.5728160623128598 | 31.74650931534741 | 50.318931638349575 | 2.334578496357329 | 5.634404078103327 | 7.093583835999231 | 1.5279327525638453 |
| user_002 | Jumping | 1.446035035417e12 | -7.1653 | -11.61046 | 0.995729 | -6.830867636363636 | -8.680694454545455 | 0.25719163636363634 | 51.34152409 | 134.8027814116 | 0.991476241441 | 13.679758102504628 | 11.519586108272428 | 0.3344323636363642 | 2.9297655454545453 | 0.7385373636363637 | 4.467641735537191 | 6.43369567768595 | 0.7800238347107438 | 0.11184500584740534 | 8.583526151332569 | 0.5454374374869505 | 27.654650666007146 | 49.408091581727376 | 0.6421275864403456 | 5.258768930653556 | 7.029088958160038 | 0.801328638225507 |
| user_002 | Jumping | 1.446035036064e12 | 0.652044 | 0.881966 | -1.11189 | -5.5419113636363635 | -6.719390818181818 | -0.2897428181818182 | 0.42516137793599995 | 0.7778640251560001 | 1.2362993721000002 | 1.561833786032304 | 9.428134272108517 | 6.193955363636364 | 7.601356818181818 | 0.8221471818181818 | 4.442940404958677 | 6.1952234876033065 | 0.7898413223140497 | 38.365083046719676 | 57.78062547731922 | 0.6759259885715785 | 26.80721992624767 | 44.61862266479724 | 0.8233101719703998 | 5.177568920472973 | 6.6797172593454315 | 0.907364409689073 |
| user_002 | Jumping | 1.446035036647e12 | -0.459245 | -2.14534 | -0.767005 | -5.242317090909091 | -6.984149545454546 | 4.4688181818181814e-2 | 0.210905970025 | 4.6024837156 | 0.5882966700250001 | 2.3241528253645454 | 9.347194647011769 | 4.783072090909091 | 4.838809545454546 | 0.8116931818181818 | 4.471758231404959 | 6.197756520661158 | 0.8256284876033056 | 22.87777862683346 | 23.41407781718203 | 0.658845821410124 | 24.532754052048745 | 43.48848930565539 | 0.8307662014794853 | 4.9530550221099645 | 6.594580297915508 | 0.9114637686049212 |
| user_002 | Jumping | 1.446035041373e12 | -0.535886 | -1.64717 | -1.03525 | -5.538427636363636 | -7.430056727272727 | -0.43257309090909096 | 0.287173804996 | 2.7131690089 | 1.0717425625 | 2.0179408753469463 | 9.764384790075978 | 5.002541636363636 | 5.782886727272727 | 0.602676909090909 | 4.196865702479339 | 5.813602826446282 | 0.5177994214876033 | 25.025422823551768 | 33.44177890046707 | 0.3632194567513718 | 22.45182419827358 | 38.34933714049541 | 0.40652773556788735 | 4.73833559367354 | 6.1926841628243405 | 0.6375952756787705 |
| user_002 | Jumping | 1.44603504215e12 | -1.14901 | -0.420925 | 0.344284 | -4.291276363636364 | -6.2247104545454555 | -0.509214 | 1.3202239801000002 | 0.177177855625 | 0.11853147265599999 | 1.2711936549483718 | 8.034460275069408 | 3.1422663636363644 | 5.803785454545455 | 0.8534980000000001 | 3.6496149421487605 | 6.059993644628099 | 0.4718784380165289 | 9.8738379000405 | 33.683925602393394 | 0.7284588360040002 | 18.037898103949008 | 41.549573370724985 | 0.2748523318328828 | 4.247104673062463 | 6.445895854784266 | 0.5242636091060325 |
| user_002 | Jumping | 1.446035042538e12 | -3.63983 | -3.86975 | -0.383802 | -5.1761277272727275 | -8.179046636363637 | -0.6102402727272728 | 13.248362428899998 | 14.974965062499999 | 0.147303975204 | 5.326408871519722 | 10.024669937451387 | 1.5362977272727276 | 4.309296636363637 | 0.2264382727272728 | 3.2996652644628095 | 5.7654663553719 | 0.49024671900826444 | 2.360210706823348 | 18.57003750017496 | 5.127429135571077e-2 | 14.209285799045562 | 38.99125749161312 | 0.2935062012731563 | 3.769520632526842 | 6.244297998303182 | 0.5417621260970135 |
| user_002 | Jumping | 1.446035042926e12 | -10.11596 | -15.25089 | 3.7722e-2 | -5.256252272727273 | -8.29400809090909 | -0.7739722727272725 | 102.33264672159999 | 232.58964579210001 | 1.422949284e-3 | 18.30092116432897 | 10.515775377709382 | 4.859707727272727 | 6.95688190909091 | 0.8116942727272726 | 3.8808028760330573 | 6.340904438016529 | 0.6865985123966943 | 23.616759194514252 | 48.39820589703639 | 0.658847592378256 | 18.500328994784294 | 45.368967180312275 | 0.8470754660074215 | 4.301200878218117 | 6.735648979891416 | 0.9203670278793246 |
| user_002 | Jumping | 1.446035043445e12 | -3.56319 | -7.81675 | -1.34181 | -4.611774090909091 | -6.754229181818181 | -1.1641431818181818 | 12.696322976100001 | 61.1015805625 | 1.8004540760999999 | 8.694731601073146 | 8.943475057995606 | 1.0485840909090913 | 1.0625208181818184 | 0.17766681818181818 | 3.6682985206611574 | 5.689458504132232 | 0.9301391074380163 | 1.0995285957076455 | 1.1289504890697608 | 3.156549828285124e-2 | 18.615440480806782 | 42.168303896146696 | 1.3272466957004447 | 4.314561447100595 | 6.493712643484211 | 1.1520619322330048 |
| user_002 | Jumping | 1.446035043574e12 | -14.36952 | -19.58108 | 0.420925 | -6.416312272727273 | -9.380912181818182 | -0.700815909090909 | 206.4831050304 | 383.4186939664 | 0.177177855625 | 24.29154126136143 | 12.046808337182016 | 7.953207727272726 | 10.200167818181818 | 1.121740909090909 | 4.3900503388429755 | 6.595844338842975 | 1.0026629669421487 | 63.25351315315061 | 104.04342351907202 | 1.2583026671280988 | 25.11940654493285 | 53.231245821914314 | 1.426878065493329 | 5.011926430518793 | 7.29597463139191 | 1.1945200146893016 |
| user_002 | Jumping | 1.446035043638e12 | -13.64143 | -17.24354 | -0.1922 | -6.736809545454546 | -9.562062181818181 | -0.721717909090909 | 186.08861244489998 | 297.3396717316 | 3.694084e-2 | 21.987842663992755 | 12.381983018969633 | 6.904620454545454 | 7.681477818181818 | 0.529517909090909 | 4.575951495867769 | 6.661716694214875 | 0.9770105702479338 | 47.67378362132747 | 59.00510147121931 | 0.2803892160480082 | 27.306408765552234 | 54.19550905593094 | 1.3924727585542156 | 5.225553441077054 | 7.3617599156676485 | 1.1800308294931179 |
| user_002 | Jumping | 1.446035044156e12 | -15.21256 | -18.89131 | -0.767005 | -6.479016363636364 | -8.948935545454544 | -0.46740981818181826 | 231.4219817536 | 356.88159351610005 | 0.5882966700250001 | 24.267094427222332 | 11.74871380185931 | 8.733543636363635 | 9.942374454545456 | 0.2995951818181818 | 4.947753066115702 | 6.188887628099172 | 0.6663303057851239 | 76.27478444826774 | 98.85080979439806 | 8.975727296866941e-2 | 29.89711555340259 | 46.60736227828711 | 0.736107912280498 | 5.467825486736257 | 6.826958493962528 | 0.8579673142261878 |
| user_002 | Jumping | 1.446035044545e12 | -0.650847 | -0.420925 | 0.267643 | -5.46527009090909 | -6.9109917272727275 | -0.8715146363636365 | 0.42360181740899994 | 0.177177855625 | 7.163277544900001e-2 | 0.8200075905032831 | 9.38892652315506 | 4.814423090909091 | 6.490066727272728 | 1.1391576363636364 | 4.232968380165289 | 6.326651388429752 | 0.7106671983471075 | 23.178669698278643 | 42.120966124452536 | 1.297680120485587 | 23.35106811043113 | 46.895908393370604 | 0.6108041687504081 | 4.8322942905447235 | 6.848058731740741 | 0.7815396143193306 |
| user_002 | Jumping | 1.446035045839e12 | -8.85139 | -11.72542 | 0.305964 | -5.521009272727273 | -7.200136545454544 | -0.35593254545454545 | 78.34710493210001 | 137.4854741764 | 9.361396929600001e-2 | 14.694427279679736 | 9.497917300387376 | 3.3303807272727273 | 4.525283454545455 | 0.6618965454545455 | 4.381500247933884 | 5.622635090909091 | 0.6425778429752066 | 11.091435788589619 | 20.478190343982853 | 0.4381070368846612 | 23.812058545512084 | 36.44259565766158 | 0.590843696719027 | 4.879760090979072 | 6.036770300223587 | 0.7686635783741982 |
| user_002 | Jumping | 1.446035046098e12 | -3.29495 | -4.59784 | -0.15388 | -5.844989272727273 | -8.078020181818182 | -0.742618909090909 | 10.856695502500001 | 21.140132665599996 | 2.3679054399999996e-2 | 5.658666558695608 | 10.38518386833713 | 2.5500392727272727 | 3.4801801818181826 | 0.588738909090909 | 4.263687842975207 | 5.87725885123967 | 0.7185846528925619 | 6.502700292451438 | 12.111654097920038 | 0.34661350307755356 | 20.709634302860838 | 39.065419343592374 | 0.8314188977151395 | 4.550783921794226 | 6.250233543123999 | 0.911821746678121 |
| user_002 | Jumping | 1.44603504681e12 | -0.229323 | 0.1922 | -0.498763 | -4.876532181818181 | -6.099299000000001 | -0.7286840000000001 | 5.2589038329e-2 | 3.694084e-2 | 0.24876453016900002 | 0.5816308180435421 | 8.625721908563833 | 4.647209181818181 | 6.291499000000001 | 0.2299210000000001 | 4.378649504132231 | 6.121748975206612 | 0.6143912975206612 | 21.59655317957521 | 39.58295966700101 | 5.286366624100004e-2 | 23.7805886174197 | 45.32605095967318 | 0.49155458285741943 | 4.876534488488695 | 6.732462473692162 | 0.7011095369893491 |
| user_002 | Jumping | 1.446035046875e12 | -0.842448 | -1.03405 | -0.345482 | -4.9636238181818175 | -6.322253545454546 | -0.6276578181818181 | 0.7097186327039999 | 1.0692594024999997 | 0.119357812324 | 1.377801091423577 | 8.565835701140356 | 4.121175818181817 | 5.288203545454547 | 0.2821758181818181 | 4.2956750578512395 | 5.860474157024794 | 0.5868385950413223 | 16.984090124366574 | 27.96509673815804 | 7.962319236657846e-2 | 23.020951708247193 | 41.81179228320366 | 0.4676545045738995 | 4.798015392664679 | 6.466203854132938 | 0.6838526921595758 |
| user_002 | Jumping | 1.446035047198e12 | -13.21991 | -16.63042 | 0.420925 | -7.189685909090908 | -9.31123781818182 | -0.6172074545454546 | 174.76602040810002 | 276.5708693764 | 0.177177855625 | 21.248860384503566 | 12.192681806384348 | 6.030224090909092 | 7.3191821818181815 | 1.0381324545454547 | 5.007293181818182 | 6.497985338842975 | 0.5244497603305786 | 36.36360258658039 | 53.57042781064475 | 1.0777189931805704 | 30.092576058743646 | 51.20800629042747 | 0.39204926912054333 | 5.485670064699812 | 7.1559769626814385 | 0.6261383785718164 |
| user_002 | Jumping | 1.446035047263e12 | -4.06135 | -5.90073 | -3.8919e-2 | -6.175939545454545 | -8.067569636363636 | -0.5301158181818182 | 16.4945638225 | 34.8186145329 | 1.514688561e-3 | 7.163427464835601 | 10.58790123368122 | 2.114589545454545 | 2.166839636363636 | 0.4911968181818182 | 4.47302656198347 | 5.761348685950413 | 0.5618199338842975 | 4.4714889457456595 | 4.695194009716494 | 0.24127431419194217 | 24.693217809402338 | 42.04669015402345 | 0.4133996612237356 | 4.969227083702489 | 6.48434192143069 | 0.6429616327773654 |
| user_002 | Jumping | 1.446035048687e12 | -0.650847 | -1.95374 | 7.6042e-2 | -4.9009182727272735 | -6.210774181818182 | -0.8749986363636364 | 0.42360181740899994 | 3.8170999876000002 | 5.782385764e-3 | 2.0606999273967572 | 8.582328109355055 | 4.250071272727274 | 4.257034181818182 | 0.9510406363636363 | 4.732400454545455 | 5.830070454545455 | 0.7724236694214877 | 18.06310582326163 | 18.1223400251684 | 0.9044782920149503 | 26.822176913056808 | 41.98446965925354 | 0.8800927343924942 | 5.179013121537423 | 6.479542395821911 | 0.938132578259861 |
| user_002 | Jumping | 1.446035049399e12 | -13.52647 | -17.3585 | -0.958607 | -6.747261090909091 | -8.673726545454544 | -0.18174954545454547 | 182.9653906609 | 301.31752224999997 | 0.918927380449 | 22.027297616624445 | 11.464334396480872 | 6.779208909090909 | 8.684773454545455 | 0.7768574545454545 | 4.79384026446281 | 5.962767528925619 | 0.6299100495867768 | 45.957673433097554 | 75.4252899567774 | 0.603507504682843 | 28.706956726588132 | 41.384309342338895 | 0.6132338470071389 | 5.357887337989492 | 6.433063760164273 | 0.7830924894334889 |
| user_002 | Jumping | 1.4460350505e12 | -6.24561 | -9.73276 | -1.15021 | -5.1587095454545455 | -7.287228 | -0.35593318181818184 | 39.0076442721 | 94.72661721760001 | 1.3229830441 | 11.62141319004707 | 9.455231050646873 | 1.0869004545454546 | 2.445532000000001 | 0.7942768181818181 | 3.9922799008264462 | 5.749314008264463 | 0.5428184214876032 | 1.1813525980911157 | 5.980626763024005 | 0.6308756639010329 | 21.690321855347296 | 39.86753617989176 | 0.427005639025329 | 4.657286962958938 | 6.314074451563884 | 0.6534566848883934 |
| user_002 | Sitting | 1.44603456478e12 | -0.765807 | -0.114362 | 9.4262 | -0.765807 | -0.114362 | 9.4262 | 0.586460361249 | 1.3078667044000002e-2 | 88.85324643999999 | 9.457948269487044 | 9.457948269487044 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_002 | Sitting | 1.446034565169e12 | -0.842448 | -0.152682 | 9.4262 | -0.8205507142857142 | -0.1417335714285714 | 9.431674285714285 | 0.7097186327039999 | 2.3311793124000002e-2 | 88.85324643999999 | 9.465002739874299 | 9.468437272237447 | 2.189728571428573e-2 | 1.0948428571428609e-2 | 5.474285714285543e-3 | 1.6631628911564604e-2 | 1.86257112244898e-2 | 1.611004081632699e-2 | 4.7949112165306194e-4 | 1.198680881836743e-4 | 2.9967804081630776e-5 | 5.057707334111117e-4 | 4.989748108321516e-4 | 4.2989052794818944e-4 | 2.2489347109489678e-2 | 2.233774408556405e-2 | 2.073380157974387e-2 |
| user_002 | Sitting | 1.446034565688e12 | -0.919089 | -3.7722e-2 | 9.4262 | -0.8389643636363636 | -0.13874772727272727 | 9.433167272727273 | 0.8447245899210001 | 1.422949284e-3 | 88.85324643999999 | 9.470976400519906 | 9.471666852469868 | 8.012463636363643e-2 | 0.10102572727272727 | 6.967272727273155e-3 | 3.203548127705629e-2 | 4.438884426406926e-2 | 1.5275845992391623e-2 | 6.4199573524049685e-3 | 1.020619757098347e-2 | 4.854288925620431e-5 | 1.4169701993062044e-3 | 2.985819414044147e-3 | 4.3332102977010464e-4 | 3.764266461485165e-2 | 5.464265196752576e-2 | 2.0816364470533866e-2 |
| user_002 | Sitting | 1.446034566658e12 | -0.727487 | -0.305964 | 9.54116 | -0.7692910909090908 | -0.16313345454545455 | 9.447101818181817 | 0.529237335169 | 9.361396929600001e-2 | 91.0337341456 | 9.57374458872102 | 9.480753256798605 | 4.180409090909076e-2 | 0.14283054545454546 | 9.405818181818226e-2 | 8.455804958677687e-2 | 5.732220661157026e-2 | 4.180363636363635e-2 | 1.747582016735525e-3 | 2.0400564714842976e-2 | 8.846941566942232e-3 | 1.325132104521563e-2 | 4.601724310780617e-3 | 2.299167754770868e-3 | 0.1151143824429234 | 6.783601042794761e-2 | 4.794963769175809e-2 |
| user_002 | Sitting | 1.446034567047e12 | -0.765807 | -0.152682 | 9.46452 | -0.7832257272727273 | -0.18055172727272728 | 9.436650909090906 | 0.586460361249 | 2.3311793124000002e-2 | 89.5771388304 | 9.496678945019307 | 9.471570465860033 | 1.7418727272727308e-2 | 2.7869727272727268e-2 | 2.7869090909094396e-2 | 5.763904132231403e-2 | 6.555633884297521e-2 | 3.705322314049604e-2 | 3.034120598016541e-4 | 7.76721698256198e-4 | 7.766862280993679e-4 | 7.076373251673926e-3 | 6.461827551674681e-3 | 1.9317863429000853e-3 | 8.412118194410921e-2 | 8.03854934156324e-2 | 4.3952091450806814e-2 |
| user_002 | Sitting | 1.446034567111e12 | -0.765807 | -0.114362 | 9.4262 | -0.75884 | -0.1666170909090909 | 9.436650909090908 | 0.586460361249 | 1.3078667044000002e-2 | 88.85324643999999 | 9.457948269487044 | 9.468972667311892 | 6.9670000000000565e-3 | 5.2255090909090904e-2 | 1.0450909090907956e-2 | 3.578690082644627e-2 | 6.143933057851239e-2 | 3.546975206611594e-2 | 4.853908900000079e-5 | 2.7305945259173546e-3 | 1.0922150082642256e-4 | 1.519209039241922e-3 | 5.845110941862509e-3 | 1.8711077313298444e-3 | 3.897703220156612e-2 | 7.645332525052465e-2 | 4.325630279311726e-2 |
| user_002 | Sitting | 1.446034567241e12 | -0.804128 | -0.114362 | 9.46452 | -0.7727747272727273 | -0.17358445454545457 | 9.440134545454544 | 0.646621840384 | 1.3078667044000002e-2 | 89.5771388304 | 9.499307308316117 | 9.473686950322545 | 3.135327272727262e-2 | 5.9222454545454564e-2 | 2.4385454545456042e-2 | 3.3253355371900814e-2 | 6.0172553719008266e-2 | 3.515305785123989e-2 | 9.83027710710737e-4 | 3.507299122388432e-3 | 5.946503933885027e-4 | 1.3095898869752052e-3 | 5.8164262194012025e-3 | 1.7949836549962473e-3 | 3.618825620246443e-2 | 7.626549822430326e-2 | 4.23672474323769e-2 |
| user_002 | Sitting | 1.446034568082e12 | -0.689167 | -0.191003 | 9.4262 | -0.7553561818181819 | -0.156166 | 9.429683636363636 | 0.47495115388899994 | 3.6482146009e-2 | 88.85324643999999 | 9.45328936084673 | 9.461340420717299 | 6.61891818181819e-2 | 3.483700000000001e-2 | 3.4836363636365775e-3 | 2.0585446280991692e-2 | 3.64201570247934e-2 | 2.406876033057886e-2 | 4.381007789760341e-3 | 1.2136165690000004e-3 | 1.2135722314051077e-5 | 7.68973975536438e-4 | 1.973744353509392e-3 | 7.92131692862547e-4 | 2.77303800106749e-2 | 4.442684271371748e-2 | 2.8144834212738702e-2 |
| user_002 | Sitting | 1.446034569054e12 | -0.765807 | -0.191003 | 9.38788 | -0.7553561818181819 | -0.14223136363636366 | 9.433167272727273 | 0.586460361249 | 3.6482146009e-2 | 88.13229089439999 | 9.420999596733777 | 9.464589357092626 | 1.0450818181818144e-2 | 4.877163636363635e-2 | 4.528727272727373e-2 | 2.7235760330578507e-2 | 3.4520107438016534e-2 | 4.1803636363636994e-2 | 1.0921960066942071e-4 | 2.3786725135867756e-3 | 2.050937071074471e-3 | 1.3459775338459804e-3 | 2.280452508866266e-3 | 2.2395923906837365e-3 | 3.6687566474842406e-2 | 4.775408368785088e-2 | 4.732433190953399e-2 |
| user_002 | Sitting | 1.446034569312e12 | -0.727487 | -0.152682 | 9.4262 | -0.7379379090909091 | -0.1352639090909091 | 9.429683636363634 | 0.529237335169 | 2.3311793124000002e-2 | 88.85324643999999 | 9.455463794457307 | 9.459572879689434 | 1.0450909090909066e-2 | 1.741809090909091e-2 | 3.483636363634801e-3 | 2.153520661157021e-2 | 2.7869371900826445e-2 | 2.4702148760331447e-2 | 1.0922150082644576e-4 | 3.0338989091735545e-4 | 1.21357223140387e-5 | 6.707759668279468e-4 | 1.524711514772352e-3 | 1.0194006743802228e-3 | 2.589934298062302e-2 | 3.904755452998756e-2 | 3.192805466012959e-2 |
| user_002 | Sitting | 1.446034569443e12 | -0.727487 | -0.152682 | 9.4262 | -0.7449052727272728 | -0.14571481818181817 | 9.429683636363636 | 0.529237335169 | 2.3311793124000002e-2 | 88.85324643999999 | 9.455463794457307 | 9.460267960270265 | 1.741827272727281e-2 | 6.9671818181818446e-3 | 3.4836363636365775e-3 | 2.3752140495867744e-2 | 1.741833884297521e-2 | 1.8051570247934832e-2 | 3.033962248016558e-4 | 4.8541622487603674e-5 | 1.2135722314051077e-5 | 8.925379239271204e-4 | 6.343758824335087e-4 | 5.858244135237135e-4 | 2.9875373201470143e-2 | 2.5186819617282145e-2 | 2.4203809896867757e-2 |
| user_002 | Sitting | 1.446034569896e12 | -0.765807 | -0.152682 | 9.4262 | -0.7518726363636362 | -0.14919836363636363 | 9.436650909090908 | 0.586460361249 | 2.3311793124000002e-2 | 88.85324643999999 | 9.458489234247349 | 9.467781289695454 | 1.39343636363638e-2 | 3.483636363636383e-3 | 1.0450909090907956e-2 | 2.4068917355371876e-2 | 1.2034322314049604e-2 | 1.74181818181824e-2 | 1.9416648995041779e-4 | 1.2135722314049723e-5 | 1.0922150082642256e-4 | 8.362799605987959e-4 | 1.8313804574830977e-4 | 4.909451299774928e-4 | 2.8918505504240636e-2 | 1.353285061427598e-2 | 2.2157281646842258e-2 |
| user_002 | Sitting | 1.446034570996e12 | -0.765807 | -0.114362 | 9.4262 | -0.7623234545454545 | -0.15964954545454543 | 9.429683636363636 | 0.586460361249 | 1.3078667044000002e-2 | 88.85324643999999 | 9.457948269487044 | 9.461895649839656 | 3.4835454545455447e-3 | 4.528754545454543e-2 | 3.4836363636365775e-3 | 1.266781818181819e-2 | 3.166985123966942e-2 | 2.6919008264463008e-2 | 1.2135088933884925e-5 | 2.050961773297518e-3 | 1.2135722314051077e-5 | 3.552524841622832e-4 | 1.5004470374380165e-3 | 1.1639260946656388e-3 | 1.884814272447774e-2 | 3.873560426065426e-2 | 3.411636109941444e-2 |
| user_002 | Sitting | 1.446034571061e12 | -0.765807 | -0.152682 | 9.46452 | -0.7623234545454545 | -0.15268218181818183 | 9.433167272727273 | 0.586460361249 | 2.3311793124000002e-2 | 89.5771388304 | 9.496678945019307 | 9.465226755939694 | 3.4835454545455447e-3 | 1.8181818181584042e-7 | 3.135272727272742e-2 | 1.266782644628101e-2 | 2.5335917355371895e-2 | 2.691900826446317e-2 | 1.2135088933884925e-5 | 3.3057851238818004e-14 | 9.829935074380258e-4 | 3.5525254174079574e-4 | 1.0591388312321563e-3 | 1.1639260946656493e-3 | 1.8848144251909674e-2 | 3.25444132107518e-2 | 3.411636109941459e-2 |
| user_002 | Sitting | 1.446034571579e12 | -0.727487 | -0.229323 | 9.4262 | -0.7623234545454545 | -0.1666169090909091 | 9.440134545454544 | 0.529237335169 | 5.2589038329e-2 | 88.85324643999999 | 9.45701183321127 | 9.472428728452309 | 3.4836454545454476e-2 | 6.27060909090909e-2 | 1.3934545454544534e-2 | 1.1401074380165273e-2 | 2.7236082644628092e-2 | 2.8185785123967057e-2 | 1.213578565297516e-3 | 3.932053837099173e-3 | 1.941715570247677e-4 | 3.5966524376709197e-4 | 1.3084813420150261e-3 | 1.1551001148009435e-3 | 1.8964842307994337e-2 | 3.617293659650853e-2 | 3.398676381771209e-2 |
| user_002 | Sitting | 1.44603457242e12 | -0.727487 | -0.152682 | 9.46452 | -0.7449052727272726 | -0.15964963636363638 | 9.429683636363633 | 0.529237335169 | 2.3311793124000002e-2 | 89.5771388304 | 9.493665675527708 | 9.460525402885743 | 1.7418272727272588e-2 | 6.96763636363637e-3 | 3.483636363636755e-2 | 3.0719429752066107e-2 | 2.4069231404958678e-2 | 2.8819173553719324e-2 | 3.0339622480164805e-4 | 4.854795649586786e-5 | 1.2135722314052313e-3 | 1.4662266727175069e-3 | 7.303726936025546e-4 | 1.2874898127723882e-3 | 3.829133939571071e-2 | 2.7025408296685448e-2 | 3.588160828018148e-2 |
| user_002 | Sitting | 1.446034572485e12 | -0.727487 | -7.6042e-2 | 9.46452 | -0.7449052727272728 | -0.14919863636363637 | 9.436650909090908 | 0.529237335169 | 5.782385764e-3 | 89.5771388304 | 9.492742414673064 | 9.46732361206275 | 1.741827272727281e-2 | 7.315663636363637e-2 | 2.786909090909262e-2 | 2.976934710743803e-2 | 2.9136322314049588e-2 | 2.6285619834711227e-2 | 3.033962248016558e-4 | 5.351893444041323e-3 | 7.766862280992689e-4 | 1.4231998481344878e-3 | 1.1893258346273481e-3 | 1.075666296018078e-3 | 3.772532104746741e-2 | 3.448660369806438e-2 | 3.279735196655483e-2 |
| user_002 | Sitting | 1.446034573133e12 | -0.765807 | -0.152682 | 9.4262 | -0.7518726363636364 | -0.1491987272727273 | 9.436650909090908 | 0.586460361249 | 2.3311793124000002e-2 | 88.85324643999999 | 9.458489234247349 | 9.467899249887088 | 1.3934363636363578e-2 | 3.4832727272727237e-3 | 1.0450909090907956e-2 | 2.6919157024793385e-2 | 3.705350413223141e-2 | 1.551801652892512e-2 | 1.941664899504116e-4 | 1.2133188892561959e-5 | 1.0922150082642256e-4 | 1.007281305137491e-3 | 1.8832758142344102e-3 | 2.7470862329075655e-4 | 3.173769533437315e-2 | 4.339672584693931e-2 | 1.6574336285075084e-2 |
| user_002 | Sitting | 1.446034573262e12 | -0.765807 | -0.114362 | 9.46452 | -0.7518725454545454 | -0.1491987272727273 | 9.440134545454544 | 0.586460361249 | 1.3078667044000002e-2 | 89.5771388304 | 9.496140155805042 | 9.471356114557238 | 1.3934454545454611e-2 | 3.483672727272728e-2 | 2.4385454545456042e-2 | 2.3435413223140508e-2 | 3.7686917355371906e-2 | 1.678479338842949e-2 | 1.9416902347934065e-4 | 1.2135975670743808e-3 | 5.946503933885027e-4 | 8.208248797753583e-4 | 1.9229953147746053e-3 | 3.1883852261458203e-4 | 2.8650041531826063e-2 | 4.385197047767187e-2 | 1.785605002834003e-2 |
| user_002 | Sitting | 1.446034573392e12 | -0.765807 | -0.152682 | 9.38788 | -0.7518725454545454 | -0.145715 | 9.433167272727273 | 0.586460361249 | 2.3311793124000002e-2 | 88.13229089439999 | 9.420300581657306 | 9.46434959120047 | 1.3934454545454611e-2 | 6.967000000000001e-3 | 4.528727272727373e-2 | 2.3435413223140508e-2 | 3.483659504132232e-2 | 1.931834710743775e-2 | 1.9416902347934065e-4 | 4.8539089000000014e-5 | 2.050937071074471e-3 | 8.119984968407226e-4 | 1.7674321221953415e-3 | 4.732931702479435e-4 | 2.849558732226312e-2 | 4.204083874276703e-2 | 2.1755302118057186e-2 |
| user_002 | Sitting | 1.446034573455e12 | -0.689167 | -0.152682 | 9.38788 | -0.7518725454545454 | -0.14223127272727273 | 9.4262 | 0.47495115388899994 | 2.3311793124000002e-2 | 88.13229089439999 | 9.414380162358698 | 9.457338697442692 | 6.270554545454543e-2 | 1.0450727272727278e-2 | 3.8320000000000576e-2 | 2.3435404958677686e-2 | 3.1669578512396705e-2 | 2.0585123966941798e-2 | 3.931985430752063e-3 | 1.0921770052892573e-4 | 1.4684224000000442e-3 | 8.119974603846732e-4 | 1.590906939531931e-3 | 5.52726989030811e-4 | 2.849556913600206e-2 | 3.988617479192422e-2 | 2.351014651232125e-2 |
| user_002 | Sitting | 1.446034573521e12 | -0.727487 | -0.114362 | 9.46452 | -0.7483889090909092 | -0.13178027272727272 | 9.429683636363636 | 0.529237335169 | 1.3078667044000002e-2 | 89.5771388304 | 9.493126715293176 | 9.460346873567627 | 2.0901909090909165e-2 | 1.7418272727272713e-2 | 3.4836363636364e-2 | 2.4068818181818195e-2 | 2.628573553719008e-2 | 2.2485289256198113e-2 | 4.368898036446312e-4 | 3.033962248016524e-4 | 1.213572231404984e-3 | 8.340632161750568e-4 | 1.0845090320075132e-3 | 6.453997776108307e-4 | 2.8880152634206364e-2 | 3.293188473208773e-2 | 2.540471959323367e-2 |
| user_002 | Sitting | 1.44603457378e12 | -0.804128 | -0.114362 | 9.4262 | -0.748388909090909 | -0.13874763636363638 | 9.426199999999996 | 0.646621840384 | 1.3078667044000002e-2 | 88.85324643999999 | 9.461128206901542 | 9.4570153963809 | 5.5739090909090905e-2 | 2.4385636363636373e-2 | 3.552713678800501e-15 | 2.5018917355371924e-2 | 3.325310743801653e-2 | 2.121851239669487e-2 | 3.1068462553719e-3 | 5.946592608595046e-4 | 1.2621774483536189e-29 | 9.057764906649138e-4 | 1.7641233586235911e-3 | 7.160076165289551e-4 | 3.0096120857428018e-2 | 4.20014685293692e-2 | 2.6758318641666466e-2 |
| user_002 | Sitting | 1.446034574557e12 | -0.727487 | -0.191003 | 9.38788 | -0.7449053636363636 | -0.16661700000000002 | 9.436650909090908 | 0.529237335169 | 3.6482146009e-2 | 88.13229089439999 | 9.417962113725983 | 9.467609675926727 | 1.741836363636362e-2 | 2.438599999999999e-2 | 4.877090909090853e-2 | 3.198629752066117e-2 | 2.6919380165289254e-2 | 2.8185785123967865e-2 | 3.033993917685945e-4 | 5.946769959999995e-4 | 2.3786015735536644e-3 | 1.3537044120946666e-3 | 1.1264441829564242e-3 | 1.1948170241924013e-3 | 3.679272227077886e-2 | 3.356254136617822e-2 | 3.456612538588034e-2 |
| user_002 | Sitting | 1.446034574622e12 | -0.765807 | -0.114362 | 9.46452 | -0.7483889999999999 | -0.16661700000000002 | 9.44361818181818 | 0.586460361249 | 1.3078667044000002e-2 | 89.5771388304 | 9.496140155805042 | 9.474829731901304 | 1.7418000000000156e-2 | 5.225500000000001e-2 | 2.0901818181819465e-2 | 3.1986272727272766e-2 | 2.913626446280992e-2 | 2.7552396694216084e-2 | 3.033867240000054e-4 | 2.730585025000001e-3 | 4.368860033058388e-4 | 1.3537035483854264e-3 | 1.304070425015778e-3 | 1.1639260946657436e-3 | 3.679271053327583e-2 | 3.611191527758917e-2 | 3.411636109941597e-2 |
| user_002 | Sitting | 1.446034576823e12 | -0.727487 | -0.191003 | 9.4262 | -0.7518726363636364 | -0.1666169090909091 | 9.422716363636361 | 0.529237335169 | 3.6482146009e-2 | 88.85324643999999 | 9.456160210211014 | 9.454272305984539 | 2.4385636363636443e-2 | 2.4386090909090913e-2 | 3.483636363638354e-3 | 2.7869264462809906e-2 | 2.7869404958677693e-2 | 2.7552396694216084e-2 | 5.94659260859508e-4 | 5.946814298264465e-4 | 1.2135722314063454e-5 | 1.0657548036927113e-3 | 1.4430797806025554e-3 | 1.042568871525224e-3 | 3.2645900258573224e-2 | 3.7987889920375356e-2 | 3.228883509086731e-2 |
| user_002 | Sitting | 1.446034577018e12 | -0.727487 | -0.114362 | 9.4262 | -0.7553562727272727 | -0.16661700000000002 | 9.408781818181815 | 0.529237335169 | 1.3078667044000002e-2 | 88.85324643999999 | 9.454922656596032 | 9.440658106002388 | 2.7869272727272687e-2 | 5.225500000000001e-2 | 1.7418181818184664e-2 | 2.4385636363636352e-2 | 3.51535041322314e-2 | 2.4385454545455074e-2 | 7.766963623471052e-4 | 2.730585025000001e-3 | 3.033930578513388e-4 | 7.822206036093154e-4 | 1.9318294136048094e-3 | 8.10886900075132e-4 | 2.7968207014560577e-2 | 4.395258142140015e-2 | 2.847607592480277e-2 |
| user_002 | Sitting | 1.446034577212e12 | -0.765807 | -0.191003 | 9.38788 | -0.75884 | -0.16661700000000002 | 9.408781818181815 | 0.586460361249 | 3.6482146009e-2 | 88.13229089439999 | 9.420999596733777 | 9.440918817313054 | 6.9670000000000565e-3 | 2.438599999999999e-2 | 2.0901818181815912e-2 | 2.6919239669421488e-2 | 2.9136363636363637e-2 | 2.3752066115702967e-2 | 4.853908900000079e-5 | 5.946769959999995e-4 | 4.3688600330569024e-4 | 9.455071044012013e-4 | 1.3239312405514654e-3 | 8.175063849737142e-4 | 3.074909924536329e-2 | 3.6385865944779515e-2 | 2.8592068567589057e-2 |
| user_002 | Sitting | 1.446034577795e12 | -0.727487 | -0.152682 | 9.4262 | -0.7483890000000002 | -0.16313327272727274 | 9.426199999999998 | 0.529237335169 | 2.3311793124000002e-2 | 88.85324643999999 | 9.455463794457307 | 9.45736017391277 | 2.0902000000000198e-2 | 1.0451272727272726e-2 | 1.7763568394002505e-15 | 2.6919165289256218e-2 | 1.9002099173553715e-2 | 2.2801983471075295e-2 | 4.3689360400000826e-4 | 1.0922910161983468e-4 | 3.1554436208840472e-30 | 1.0315537862246422e-3 | 4.5014236270323057e-4 | 1.081182533433542e-3 | 3.211781104347932e-2 | 2.121655869134367e-2 | 3.28813402012987e-2 |
| user_002 | Sitting | 1.446034578183e12 | -0.765807 | -0.191003 | 9.46452 | -0.7414216363636363 | -0.15268236363636364 | 9.429683636363636 | 0.586460361249 | 3.6482146009e-2 | 89.5771388304 | 9.497372338581762 | 9.460134033566653 | 2.4385363636363677e-2 | 3.832063636363636e-2 | 3.4836363636364e-2 | 2.7235842975206627e-2 | 2.8502917355371894e-2 | 1.9635041322314608e-2 | 5.94645959677688e-4 | 1.4684711713140496e-3 | 1.213572231404984e-3 | 9.598359052314046e-4 | 1.2091844044785874e-3 | 6.509160150263041e-4 | 3.09812185885482e-2 | 3.477332892431479e-2 | 2.5513055775941543e-2 |
| user_002 | Sitting | 1.446034578766e12 | -0.765807 | -0.191003 | 9.4262 | -0.7623234545454545 | -0.1666170909090909 | 9.4262 | 0.586460361249 | 3.6482146009e-2 | 88.85324643999999 | 9.459185427258417 | 9.45851288519757 | 3.4835454545455447e-3 | 2.4385909090909097e-2 | 0.0 | 1.7101537190082703e-2 | 2.501930578512396e-2 | 2.7552396694215112e-2 | 1.2135088933884925e-5 | 5.94672562190083e-4 | 0.0 | 4.6778447599248973e-4 | 8.528350068084148e-4 | 1.2411534184823567e-3 | 2.162832577876729e-2 | 2.9203338966775953e-2 | 3.523000735853395e-2 |
| user_002 | Sitting | 1.446034581033e12 | -0.727487 | -0.191003 | 9.4262 | -0.7623234545454545 | -0.17010063636363637 | 9.478454545454545 | 0.529237335169 | 3.6482146009e-2 | 88.85324643999999 | 9.456160210211014 | 9.510648892733103 | 3.4836454545454476e-2 | 2.0902363636363636e-2 | 5.225454545454511e-2 | 1.741826446280995e-2 | 2.4068983471074386e-2 | 2.4702148760330965e-2 | 1.213578565297516e-3 | 4.369088055867768e-4 | 2.730537520661121e-3 | 4.4682294655447097e-4 | 1.0150141718414728e-3 | 1.1650293421487457e-3 | 2.1138186926850443e-2 | 3.185928705795961e-2 | 3.413252616125483e-2 |
| user_002 | Sitting | 1.446309384445e12 | -0.535886 | 3.2195 | 9.04299 | -0.7309707272727273 | 0.7809393636363636 | 9.339108181818183 | 0.287173804996 | 10.36518025 | 81.7756681401 | 9.613949354718693 | 9.520764338343959 | 0.1950847272727273 | 2.4385606363636363 | 0.2961181818181835 | 7.220688429752066e-2 | 0.7740065371900826 | 9.595917355371855e-2 | 3.805805081507439e-2 | 5.946577977222223 | 8.768597760330678e-2 | 8.956297980089407e-3 | 2.098633624418471 | 2.3968541014275056e-2 | 9.4637719647556e-2 | 1.4486661535421028 | 0.15481776711435627 |
| user_002 | Sitting | 1.44630938464e12 | -0.574206 | 3.2195 | 9.04299 | -0.6891668181818181 | 1.6901753636363637 | 9.234597272727274 | 0.329712530436 | 10.36518025 | 81.7756681401 | 9.616161444180104 | 9.565677478020099 | 0.11496081818181814 | 1.5293246363636364 | 0.19160727272727485 | 9.627590082644626e-2 | 1.260769123966942 | 0.14789826446281007 | 1.3215989717033047e-2 | 2.3388338433887688 | 3.6713346961984285e-2 | 1.2208727644145751e-2 | 3.044968591590844 | 3.636520440157808e-2 | 0.11049311129724672 | 1.744983837057193 | 0.19069662923496597 |
| user_002 | Sitting | 1.446309385353e12 | -0.574206 | 3.2195 | 9.15796 | -0.5602713636363635 | 3.2334381818181814 | 9.098737272727274 | 0.329712530436 | 10.36518025 | 83.86823136159998 | 9.724357261127132 | 9.672577064669866 | 1.3934636363636455e-2 | 1.3938181818181405e-2 | 5.9222727272725706e-2 | 4.877124793388429e-2 | 0.2900964710743801 | 5.162198347107379e-2 | 1.941740905867794e-4 | 1.942729123966827e-4 | 3.507331425619649e-3 | 3.8702471030300487e-3 | 0.2611054662973247 | 3.4476748824943267e-3 | 6.221131008932418e-2 | 0.5109848004562608 | 5.871690457180391e-2 |
| user_002 | Sitting | 1.446309386194e12 | -0.574206 | 3.25783 | 9.15796 | -0.5707223636363637 | 3.216019090909091 | 9.154476363636364 | 0.329712530436 | 10.613456308899998 | 83.86823136159998 | 9.73711457265118 | 9.719867683131518 | 3.4836363636362444e-3 | 4.181090909090868e-2 | 3.483636363634801e-3 | 2.2485289256198335e-2 | 3.674115702479346e-2 | 2.9135950413221225e-2 | 1.2135722314048756e-5 | 1.74815211900823e-3 | 1.21357223140387e-5 | 7.138006609226137e-4 | 1.8670350664162334e-3 | 1.1142885951163555e-3 | 2.6717048132655182e-2 | 4.320920117771484e-2 | 3.338096156668282e-2 |
| user_002 | Sitting | 1.446309386519e12 | -0.612526 | 3.2195 | 9.15796 | -0.5776896363636363 | 3.2125354545454545 | 9.161443636363638 | 0.375188100676 | 10.36518025 | 83.86823136159998 | 9.726695210207627 | 9.725675974401412 | 3.4836363636363665e-2 | 6.964545454545501e-3 | 3.483636363638354e-3 | 2.2801975206611588e-2 | 3.674016528925614e-2 | 2.6919008264462363e-2 | 1.2135722314049607e-3 | 4.85048933884304e-5 | 1.2135722314063454e-5 | 7.347622479376416e-4 | 1.7588897474079622e-3 | 1.2764573379413628e-3 | 2.7106498260336793e-2 | 4.1939119535440446e-2 | 3.572754312769579e-2 |
| user_002 | Sitting | 1.446309386648e12 | -0.574206 | 3.18118 | 9.15796 | -0.5776896363636363 | 3.1951163636363633 | 9.164927272727274 | 0.329712530436 | 10.1199061924 | 83.86823136159998 | 9.711737747923179 | 9.72320484422079 | 3.4836363636363554e-3 | 1.3936363636363414e-2 | 6.967272727274931e-3 | 2.3752057851239683e-2 | 3.610619834710726e-2 | 2.6285619834710904e-2 | 1.213572231404953e-5 | 1.9422223140495246e-4 | 4.854288925622906e-5 | 8.042663787250196e-4 | 1.7125032538692574e-3 | 1.2764573379413818e-3 | 2.8359590595158805e-2 | 4.1382402707784594e-2 | 3.5727543127696056e-2 |
| user_002 | Sitting | 1.44630938775e12 | -0.612526 | 3.2195 | 9.15796 | -0.6090425454545454 | 3.2299545454545453 | 9.185829090909092 | 0.375188100676 | 10.36518025 | 83.86823136159998 | 9.726695210207627 | 9.756244658044482 | 3.483454545454623e-3 | 1.0454545454545272e-2 | 2.786909090909262e-2 | 2.2168702479338845e-2 | 2.217214876033062e-2 | 2.913586776859457e-2 | 1.2134455570248473e-5 | 1.0929752066115321e-4 | 7.766862280992689e-4 | 7.612562527084889e-4 | 8.453236761081932e-4 | 1.0061617045830013e-3 | 2.7590872634052167e-2 | 2.9074450572765656e-2 | 3.1720052089853214e-2 |
| user_002 | Sitting | 1.446309387944e12 | -0.612526 | 3.2195 | 9.19628 | -0.6055588181818181 | 3.2299545454545453 | 9.178861818181819 | 0.375188100676 | 10.36518025 | 84.57156583839999 | 9.762783116974175 | 9.749457085970963 | 6.9671818181819e-3 | 1.0454545454545272e-2 | 1.741818181818111e-2 | 1.6784818181818206e-2 | 2.058909090909091e-2 | 2.6602314049586958e-2 | 4.8541622487604446e-5 | 1.0929752066115321e-4 | 3.0339305785121503e-4 | 4.1151804822614626e-4 | 7.051859090909096e-4 | 8.693590166792054e-4 | 2.0285907626383055e-2 | 2.6555336734654857e-2 | 2.948489472050401e-2 |
| user_002 | Sitting | 1.446309388852e12 | -0.650847 | 3.2195 | 9.19628 | -0.605558909090909 | 3.1985981818181823 | 9.178861818181819 | 0.42360181740899994 | 10.36518025 | 84.57156583839999 | 9.765262306042219 | 9.739133143241268 | 4.528809090909092e-2 | 2.090181818181769e-2 | 1.741818181818111e-2 | 2.1218752066115697e-2 | 2.09031404958675e-2 | 3.198611570247953e-2 | 2.0510111781900835e-3 | 4.368860033057645e-4 | 3.0339305785121503e-4 | 9.432911661134501e-4 | 5.847724156273373e-4 | 1.7001043714500528e-3 | 3.0713045536277416e-2 | 2.4182068059356243e-2 | 4.123232192649418e-2 |
| user_002 | Sitting | 1.446309389111e12 | -0.612526 | 3.06622 | 9.19628 | -0.6020752727272728 | 3.1776963636363633 | 9.178861818181817 | 0.375188100676 | 9.4017050884 | 84.57156583839999 | 9.713313493729933 | 9.73214500267062 | 1.0450727272727223e-2 | 0.11147636363636337 | 1.7418181818182887e-2 | 2.5969099173553722e-2 | 3.388677685950394e-2 | 2.7869090909091166e-2 | 1.0921770052892458e-4 | 1.2426979649586719e-2 | 3.0339305785127694e-4 | 1.1209155655725032e-3 | 1.863416669120946e-3 | 1.3945048186326265e-3 | 3.3480077144064396e-2 | 4.316731019094132e-2 | 3.734306921816452e-2 |
| user_002 | Sitting | 1.446309389241e12 | -0.574206 | 3.18118 | 9.15796 | -0.5985916363636363 | 3.1811800000000003 | 9.16841090909091 | 0.329712530436 | 10.1199061924 | 83.86823136159998 | 9.711737747923179 | 9.723228537958677 | 2.438563636363633e-2 | 4.440892098500626e-16 | 1.0450909090911509e-2 | 2.8819330578512402e-2 | 3.1036198347107306e-2 | 2.6285619834711064e-2 | 5.946592608595026e-4 | 1.9721522630525295e-31 | 1.0922150082649681e-4 | 1.183801075178814e-3 | 1.7762370850488212e-3 | 1.298522287603321e-3 | 3.4406410379154845e-2 | 4.214542780716339e-2 | 3.603501474404196e-2 |
| user_002 | Sitting | 1.44630938937e12 | -0.650847 | 3.2195 | 9.15796 | -0.605559 | 3.1881472727272726 | 9.164927272727274 | 0.42360181740899994 | 10.36518025 | 83.86823136159998 | 9.72918359519487 | 9.722676512480279 | 4.5287999999999995e-2 | 3.135272727272742e-2 | 6.967272727274931e-3 | 3.0402834710743808e-2 | 3.23028099173553e-2 | 2.4385454545455234e-2 | 2.0510029439999994e-3 | 9.829935074380258e-4 | 4.854288925622906e-5 | 1.2533105034477844e-3 | 1.8512492766340983e-3 | 1.249979398347126e-3 | 3.5402125691090705e-2 | 4.3026146430212624e-2 | 3.535504770675788e-2 |
| user_002 | Sitting | 1.446309389629e12 | -0.612526 | 3.2195 | 9.19628 | -0.6125262727272726 | 3.2020827272727272 | 9.178861818181819 | 0.375188100676 | 10.36518025 | 84.57156583839999 | 9.762783116974175 | 9.740828570831246 | 2.727272725433494e-7 | 1.741727272727278e-2 | 1.741818181818111e-2 | 2.565241322314048e-2 | 3.7053966942148756e-2 | 1.995173553719034e-2 | 7.438016518893438e-14 | 3.0336138925620023e-4 | 3.0339305785121503e-4 | 1.1727739554079622e-3 | 2.5872794606310967e-3 | 5.262490494365031e-4 | 3.424578741112492e-2 | 5.086530704351539e-2 | 2.294011877555352e-2 |
| user_002 | Sitting | 1.446309390018e12 | -0.574206 | 3.25783 | 9.15796 | -0.5985916363636364 | 3.2195027272727272 | 9.171894545454547 | 0.329712530436 | 10.613456308899998 | 83.86823136159998 | 9.73711457265118 | 9.739064553708165 | 2.4385636363636443e-2 | 3.832727272727254e-2 | 1.3934545454548086e-2 | 2.6285809917355386e-2 | 3.388867768595041e-2 | 2.058512396694293e-2 | 5.94659260859508e-4 | 1.4689798347107296e-3 | 1.9417155702486672e-4 | 1.1484991710375648e-3 | 1.78206255048835e-3 | 5.615529688955879e-4 | 3.388951417529565e-2 | 4.2214482710183125e-2 | 2.3697108872087917e-2 |
| user_002 | Sitting | 1.446309391119e12 | -0.574206 | 3.10454 | 9.15796 | -0.5567876363636364 | 3.1846645454545457 | 9.144025454545456 | 0.329712530436 | 9.638168611600001 | 83.86823136159998 | 9.686904175413112 | 9.698858991586967 | 1.741836363636362e-2 | 8.012454545454561e-2 | 1.3934545454542757e-2 | 3.293638842975204e-2 | 2.2486859504132376e-2 | 1.5201322314048905e-2 | 3.033993917685945e-4 | 6.419942784297547e-3 | 1.9417155702471822e-4 | 1.478373393021036e-3 | 1.071374233433505e-3 | 3.6848465935387405e-4 | 3.844962149385916e-2 | 3.273185349828978e-2 | 1.9195954244420204e-2 |
| user_002 | Sitting | 1.446309392026e12 | -0.535886 | 3.2195 | 9.11964 | -0.5533039999999999 | 3.1846645454545457 | 9.144025454545456 | 0.287173804996 | 10.36518025 | 83.1678337296 | 9.686082168998775 | 9.698729922258241 | 1.7417999999999934e-2 | 3.4835454545454336e-2 | 2.4385454545456042e-2 | 3.515319008264461e-2 | 3.1037024793388455e-2 | 4.3070413223140716e-2 | 3.033867239999977e-4 | 1.2135088933884152e-3 | 5.946503933885027e-4 | 1.567734366244927e-3 | 1.279874187678435e-3 | 2.2837222900075043e-3 | 3.959462547170925e-2 | 3.577532931614236e-2 | 4.77883070427014e-2 |
| user_002 | Sitting | 1.44630939248e12 | -0.612526 | 3.18118 | 9.15796 | -0.5672386363636364 | 3.163761818181818 | 9.14054181818182 | 0.375188100676 | 10.1199061924 | 83.86823136159998 | 9.714078734222612 | 9.689318632790359 | 4.528736363636365e-2 | 1.7418181818182e-2 | 1.7418181818179335e-2 | 3.103615702479341e-2 | 2.1218677685950326e-2 | 3.0402644628098455e-2 | 2.050945305132233e-3 | 3.03393057851246e-4 | 3.033930578511532e-4 | 1.2621281915815193e-3 | 5.747948180315491e-4 | 1.5489594662659756e-3 | 3.552644355380256e-2 | 2.397487889503405e-2 | 3.9356822359865075e-2 |
| user_002 | Sitting | 1.446309392932e12 | -0.497565 | 3.14286 | 9.15796 | -0.5672386363636364 | 3.1672463636363632 | 9.133574545454547 | 0.24757092922499999 | 9.877568979600001 | 83.86823136159998 | 9.695017858179787 | 9.68388161177369 | 6.967363636363638e-2 | 2.438636363636304e-2 | 2.438545454545249e-2 | 2.3752181818181853e-2 | 2.8186694214875872e-2 | 3.325289256198293e-2 | 4.854415604132234e-3 | 5.946947314049296e-4 | 5.946503933883295e-4 | 9.89626005675433e-4 | 1.2677851063110273e-3 | 1.459596420135237e-3 | 3.1458321723757496e-2 | 3.5605970093665854e-2 | 3.8204664900182504e-2 |
| user_002 | Sitting | 1.446309393711e12 | -0.535886 | 3.14286 | 9.15796 | -0.556787909090909 | 3.1742136363636364 | 9.137058181818183 | 0.287173804996 | 9.877568979600001 | 83.86823136159998 | 9.697060077476884 | 9.688865473566398 | 2.0901909090909054e-2 | 3.1353636363636195e-2 | 2.0901818181815912e-2 | 2.7552495867768565e-2 | 3.452090909090896e-2 | 2.8502479338842302e-2 | 4.368898036446266e-4 | 9.830505132231299e-4 | 4.3688600330569024e-4 | 1.119810475468068e-3 | 1.5546162080390586e-3 | 1.0547045938392187e-3 | 3.3463569377280546e-2 | 3.9428621685763485e-2 | 3.2476215817721414e-2 |
| user_002 | Sitting | 1.446309393905e12 | -0.574206 | 3.18118 | 9.11964 | -0.5637551818181818 | 3.1776972727272725 | 9.13009090909091 | 0.329712530436 | 10.1199061924 | 83.1678337296 | 9.67561121854511 | 9.683824991940147 | 1.0450818181818144e-2 | 3.4827272727273595e-3 | 1.0450909090909732e-2 | 2.3752148760330553e-2 | 3.198719008264464e-2 | 2.2485289256198113e-2 | 1.0921960066942071e-4 | 1.2129389256198951e-5 | 1.092215008264597e-4 | 9.477042135897816e-4 | 1.4509074897821168e-3 | 6.983556567994071e-4 | 3.07848049139471e-2 | 3.8090779590107066e-2 | 2.6426419674246585e-2 |
| user_002 | Sitting | 1.446309394877e12 | -0.574206 | 3.18118 | 9.19628 | -0.5811733636363636 | 3.1881472727272726 | 9.157960000000001 | 0.329712530436 | 10.1199061924 | 84.57156583839999 | 9.747881029292264 | 9.714549440349064 | 6.967363636363633e-3 | 6.967272727272711e-3 | 3.83199999999988e-2 | 2.628568595041322e-2 | 1.741818181818192e-2 | 3.2302809917355096e-2 | 4.854415604132226e-5 | 4.854288925619812e-5 | 1.468422399999908e-3 | 1.1705577291855754e-3 | 5.174230695717485e-4 | 1.2047462515401587e-3 | 3.4213414462540497e-2 | 2.2746935388569346e-2 | 3.470945478598243e-2 |
| user_002 | Sitting | 1.446309396108e12 | -0.574206 | 3.2195 | 9.15796 | -0.5707222727272727 | 3.1951154545454545 | 9.130090909090912 | 0.329712530436 | 10.36518025 | 83.86823136159998 | 9.724357261127132 | 9.689957950948695 | 3.4837272727272772e-3 | 2.4384545454545492e-2 | 2.786909090908729e-2 | 2.47022479338843e-2 | 2.5653553719008248e-2 | 3.1352727272726776e-2 | 1.2136355710743833e-5 | 5.946060570247952e-4 | 7.766862280989719e-4 | 1.059130078879039e-3 | 8.617503026296023e-4 | 1.2985222876033054e-3 | 3.254427874264598e-2 | 2.935558384072104e-2 | 3.6035014744041735e-2 |
| user_002 | Sitting | 1.44630939669e12 | -0.574206 | 3.2195 | 9.11964 | -0.5637550909090909 | 3.2020827272727272 | 9.130090909090912 | 0.329712530436 | 10.36518025 | 83.1678337296 | 9.688277788649332 | 9.69178806671138 | 1.0450909090909066e-2 | 1.741727272727278e-2 | 1.0450909090911509e-2 | 1.8368264462809904e-2 | 1.9952644628099234e-2 | 1.900165289256186e-2 | 1.0922150082644576e-4 | 3.0336138925620023e-4 | 1.0922150082649681e-4 | 6.023727802885044e-4 | 5.52808184748308e-4 | 5.758951861757681e-4 | 2.4543283812246974e-2 | 2.3511873271781388e-2 | 2.3997816279315253e-2 |
| user_002 | Sitting | 1.446309397792e12 | -0.650847 | 3.18118 | 9.11964 | -0.5811733636363636 | 3.181180909090909 | 9.137058181818183 | 0.42360181740899994 | 10.1199061924 | 83.1678337296 | 9.680461855686897 | 9.692544633867705 | 6.967363636363633e-2 | 9.0909090921798e-7 | 1.7418181818182887e-2 | 2.0901900826446305e-2 | 2.2486611570248005e-2 | 2.8185785123966575e-2 | 4.854415604132226e-3 | 8.264462812227735e-13 | 3.0339305785127694e-4 | 9.620433214876044e-4 | 1.0404994267468033e-3 | 1.2786638329075584e-3 | 3.1016823201088863e-2 | 3.225677334679963e-2 | 3.575840926142491e-2 |
| user_002 | Sitting | 1.446309397857e12 | -0.574206 | 3.18118 | 9.11964 | -0.5776897272727273 | 3.181180909090909 | 9.14054181818182 | 0.329712530436 | 10.1199061924 | 83.1678337296 | 9.67561121854511 | 9.695612912433567 | 3.4837272727272772e-3 | 9.0909090921798e-7 | 2.0901818181819465e-2 | 1.741827272727274e-2 | 2.1536528925619927e-2 | 2.6602314049586472e-2 | 1.2136355710743833e-5 | 8.264462812227735e-13 | 4.368860033058388e-4 | 8.042789889864763e-4 | 1.0305684719759548e-3 | 1.184887796844449e-3 | 2.835981292227571e-2 | 3.210246831594036e-2 | 3.4422199186636075e-2 |
| user_002 | Sitting | 1.446309398115e12 | -0.612526 | 3.10454 | 9.11964 | -0.584657 | 3.1776972727272734 | 9.133574545454548 | 0.375188100676 | 9.638168611600001 | 83.1678337296 | 9.653040476548103 | 9.688387501624 | 2.7869000000000033e-2 | 7.315727272727335e-2 | 1.3934545454548086e-2 | 3.008621487603306e-2 | 2.407008264462823e-2 | 2.4385454545455234e-2 | 7.766811610000018e-4 | 5.351986552892653e-3 | 1.9417155702486672e-4 | 1.797227750449286e-3 | 1.3549278384673222e-3 | 8.550167993989645e-4 | 4.239372300764921e-2 | 3.680934444495476e-2 | 2.924067029667693e-2 |
| user_002 | Sitting | 1.446309398569e12 | -0.612526 | 3.2195 | 9.11964 | -0.5951078181818181 | 3.1846636363636365 | 9.109188181818183 | 0.375188100676 | 10.36518025 | 83.1678337296 | 9.690624442226413 | 9.668323338838917 | 1.7418181818181888e-2 | 3.4836363636363554e-2 | 1.0451818181817174e-2 | 2.786923966942151e-2 | 3.1036363636363692e-2 | 2.945347107438097e-2 | 3.033930578512421e-4 | 1.213572231404953e-3 | 1.0924050330576405e-4 | 1.326128349746056e-3 | 1.646059640571004e-3 | 1.2214061022539973e-3 | 3.6416045223857794e-2 | 4.057166055969368e-2 | 3.494862089201801e-2 |
| user_002 | Sitting | 1.446309399282e12 | -0.497565 | 3.2195 | 9.11964 | -0.563755 | 3.198599090909091 | 9.119639090909093 | 0.24757092922499999 | 10.36518025 | 83.1678337296 | 9.684037634624568 | 9.680833551221772 | 6.619000000000003e-2 | 2.0900909090908915e-2 | 9.090909074416231e-7 | 2.5969000000000016e-2 | 1.7419008264462865e-2 | 2.565413223140357e-2 | 4.381116100000004e-3 | 4.368480008264389e-4 | 8.264462779930337e-13 | 1.1915174160338085e-3 | 5.52832369797143e-4 | 1.0603619127722878e-3 | 3.451836346111745e-2 | 2.3512387581807657e-2 | 3.256319874908311e-2 |
| user_002 | Sitting | 1.446309399346e12 | -0.574206 | 3.18118 | 9.08132 | -0.563755 | 3.198599090909091 | 9.123123636363637 | 0.329712530436 | 10.1199061924 | 82.47037294239999 | 9.639501629505334 | 9.684115539772451 | 1.0450999999999988e-2 | 1.7419090909091217e-2 | 4.180363636363715e-2 | 2.501892561983473e-2 | 1.8685867768595103e-2 | 2.4069917355370472e-2 | 1.0922340099999975e-4 | 3.034247280991843e-4 | 1.7475440132232066e-3 | 1.1617299067332827e-3 | 5.793131885048824e-4 | 9.003029230652933e-4 | 3.40841591759762e-2 | 2.4068925786268118e-2 | 3.000504829300052e-2 |
| user_002 | Sitting | 1.446309399411e12 | -0.574206 | 3.2195 | 9.19628 | -0.5672386363636365 | 3.2020827272727272 | 9.13009090909091 | 0.329712530436 | 10.36518025 | 84.57156583839999 | 9.760453812135786 | 9.692028365975302 | 6.967363636363522e-3 | 1.741727272727278e-2 | 6.618909090908964e-2 | 2.1218611570247944e-2 | 1.995256198347112e-2 | 2.8820165289254716e-2 | 4.8544156041320715e-5 | 3.0336138925620023e-4 | 4.380995755371733e-3 | 9.49907311271224e-4 | 6.057882491359869e-4 | 1.2809186979713656e-3 | 3.0820566368436903e-2 | 2.4612765978979018e-2 | 3.578992453151258e-2 |
| user_002 | Sitting | 1.446309399929e12 | -0.574206 | 3.14286 | 9.08132 | -0.5672386363636365 | 3.195115454545454 | 9.109189090909092 | 0.329712530436 | 9.877568979600001 | 82.47037294239999 | 9.626923415735476 | 9.670031425217148 | 6.967363636363522e-3 | 5.225545454545388e-2 | 2.786909090909262e-2 | 1.3301322314049503e-2 | 2.7553388429752038e-2 | 2.94526446280997e-2 | 4.8544156041320715e-5 | 2.730632529751997e-3 | 7.766862280992689e-4 | 4.5896321775356784e-4 | 1.0139892420736236e-3 | 1.2610118732532096e-3 | 2.142342684431153e-2 | 3.18431977363082e-2 | 3.5510728987915886e-2 |
| user_002 | Sitting | 1.446309400059e12 | -0.574206 | 3.18118 | 9.11964 | -0.5742060000000001 | 3.195115454545454 | 9.116156363636366 | 0.329712530436 | 10.1199061924 | 83.1678337296 | 9.67561121854511 | 9.676979535721001 | 1.1102230246251565e-16 | 1.3935454545454196e-2 | 3.483636363634801e-3 | 6.3339586776858855e-3 | 2.7553388429752e-2 | 3.0085950413223535e-2 | 1.232595164407831e-32 | 1.9419689338842001e-4 | 1.21357223140387e-5 | 5.075053584447656e-5 | 1.0184010803906773e-3 | 1.2896963077385455e-3 | 7.123941033197605e-3 | 3.191239697031042e-2 | 3.5912341997404534e-2 |
| user_002 | Standing | 1.446034733761e12 | -1.45557 | -9.4262 | -5.99e-4 | -1.46515 | -9.40704 | 0.1718425 | 2.1186840249000003 | 88.85324643999999 | 3.5880100000000004e-7 | 9.537920676106559 | 9.522599287107857 | 9.579999999999922e-3 | 1.91599999999994e-2 | 0.1724415 | 1.0378333333333323e-2 | 4.470666666666645e-2 | 5.7480375e-2 | 9.177639999999851e-5 | 3.67105599999977e-4 | 2.973607092225e-2 | 1.5551001111111117e-4 | 5.119083644444456e-3 | 7.8928997305625e-3 | 1.2470365315864294e-2 | 7.154777176435655e-2 | 8.884199305825202e-2 |
| user_002 | Standing | 1.446034734278e12 | -1.49389 | -9.38788 | 0.191003 | -1.4625372727272727 | -9.41574909090909 | 0.15616609090909092 | 2.2317073320999996 | 88.13229089439999 | 3.6482146009e-2 | 9.507916720949389 | 9.530694554162094 | 3.13527272727272e-2 | 2.7869090909090843e-2 | 3.4836909090909085e-2 | 4.5657754689754686e-2 | 4.277432926669323e-2 | 7.490013344155845e-2 | 9.82993507438012e-4 | 7.766862280991699e-4 | 1.213610235008264e-3 | 3.2685541165727136e-3 | 3.033036308729061e-3 | 8.322194092951866e-3 | 5.717127002763463e-2 | 5.507300889482125e-2 | 9.122606038272105e-2 |
| user_002 | Standing | 1.44603473512e12 | -1.34061 | -9.38788 | 0.344284 | -1.4381518181818183 | -9.401814545454544 | 0.13178045454545456 | 1.7972351721000002 | 88.13229089439999 | 0.11853147265599999 | 9.489365497184519 | 9.512845362386004 | 9.754181818181817e-2 | 1.3934545454544534e-2 | 0.21250354545454542 | 4.750413223140511e-2 | 3.325289256198293e-2 | 8.012428099173553e-2 | 9.514406294214874e-3 | 1.941715570247677e-4 | 4.515775683075205e-2 | 3.3494593586777003e-3 | 1.41546652081137e-3 | 1.0916814534482344e-2 | 5.787451389582206e-2 | 3.7622686251932755e-2 | 0.1044835610729379 |
| user_002 | Standing | 1.446034735768e12 | -1.41725 | -9.38788 | 0.114362 | -1.3963481818181818 | -9.408781818181815 | 0.1631333636363637 | 2.0085975625 | 88.13229089439999 | 1.3078667044000002e-2 | 9.494944292829947 | 9.514008835017863 | 2.0901818181818133e-2 | 2.0901818181815912e-2 | 4.877136363636368e-2 | 3.451966942148756e-2 | 3.483636363636384e-2 | 9.437555371900828e-2 | 4.3688600330578305e-4 | 4.3688600330569024e-4 | 2.3786459109504175e-3 | 2.1303708898572473e-3 | 1.804912882344121e-3 | 1.3693708602697223e-2 | 4.6155941002835675e-2 | 4.248426629170052e-2 | 0.11702012050368613 |
| user_002 | Standing | 1.446034736479e12 | -1.34061 | -9.46452 | 3.7722e-2 | -1.3754463636363636 | -9.422716363636363 | 0.1666170909090909 | 1.7972351721000002 | 89.5771388304 | 1.422949284e-3 | 9.559068832882417 | 9.524670474321272 | 3.4836363636363554e-2 | 4.180363636363715e-2 | 0.1288950909090909 | 6.27054545454545e-2 | 3.420297520661189e-2 | 4.085400826446281e-2 | 1.213572231404953e-3 | 1.7475440132232066e-3 | 1.661394446046281e-2 | 8.300834062809898e-3 | 1.7232725685950516e-3 | 2.6687947015815176e-3 | 9.110891319080641e-2 | 4.1512318275363176e-2 | 5.166037844984798e-2 |
| user_002 | Standing | 1.446034737063e12 | -1.37893 | -9.4262 | 0.191003 | -1.37893 | -9.4262 | 0.16313336363636363 | 1.9014479449 | 88.85324643999999 | 3.6482146009e-2 | 9.528440403912331 | 9.528531738553587 | 0.0 | 0.0 | 2.7869636363636374e-2 | 5.732165289256191e-2 | 2.913586776859586e-2 | 4.750463636363636e-2 | 0.0 | 0.0 | 7.767166310413229e-4 | 6.5786647416979545e-3 | 1.5268945166040599e-3 | 3.506169387605559e-3 | 8.110896831854018e-2 | 3.9075497650113936e-2 | 5.921291571613037e-2 |
| user_002 | Standing | 1.446034737451e12 | -1.37893 | -9.46452 | 0.191003 | -1.3824136363636361 | -9.422716363636363 | 0.18403536363636366 | 1.9014479449 | 89.5771388304 | 3.6482146009e-2 | 9.56635086756225 | 9.525611176471477 | 3.4836363636361334e-3 | 4.180363636363715e-2 | 6.9676363636363425e-3 | 4.623735537190076e-2 | 3.166942148760428e-2 | 2.7236190082644628e-2 | 1.2135722314047982e-5 | 1.7475440132232066e-3 | 4.8547956495867476e-5 | 3.252373580165281e-3 | 1.456286677685994e-3 | 1.25110982419459e-3 | 5.7029585130573074e-2 | 3.816132437017869e-2 | 3.5371030861350225e-2 |
| user_002 | Standing | 1.446034737969e12 | -1.37893 | -9.46452 | 0.305964 | -1.3649954545454546 | -9.40529818181818 | 0.19796990909090909 | 1.9014479449 | 89.5771388304 | 9.361396929600001e-2 | 9.569336484030437 | 9.506087003781976 | 1.3934545454545422e-2 | 5.922181818182004e-2 | 0.10799409090909093 | 2.4068760330578517e-2 | 3.483636363636335e-2 | 3.863722314049587e-2 | 1.9417155702479248e-4 | 3.507223748760551e-3 | 1.1662723671280996e-2 | 1.383472343801649e-3 | 1.4761451323816782e-3 | 2.290394691998498e-3 | 3.7195058056167206e-2 | 3.84206342006698e-2 | 4.785806820169926e-2 |
| user_002 | Standing | 1.446034738293e12 | -1.37893 | -9.38788 | 0.229323 | -1.3545445454545457 | -9.40529818181818 | 0.2014535454545455 | 1.9014479449 | 88.13229089439999 | 5.2589038329e-2 | 9.491381768616675 | 9.50469796488644 | 2.4385454545454266e-2 | 1.741818181818111e-2 | 2.7869454545454503e-2 | 2.850247933884299e-2 | 2.7869090909090843e-2 | 4.4654388429752064e-2 | 5.946503933884161e-4 | 3.0339305785121503e-4 | 7.767064966611546e-4 | 1.7034141138993234e-3 | 1.1032474830954312e-3 | 2.5540784987310283e-3 | 4.127243770241011e-2 | 3.321516947262849e-2 | 5.0537891712367944e-2 |
| user_002 | Standing | 1.446034738941e12 | -1.37893 | -9.4262 | 0.229323 | -1.368479090909091 | -9.401814545454544 | 0.22235563636363634 | 1.9014479449 | 88.85324643999999 | 5.2589038329e-2 | 9.529285567304036 | 9.503629742042433 | 1.0450909090909066e-2 | 2.4385454545456042e-2 | 6.96736363636366e-3 | 3.4519669421487584e-2 | 3.1036033057851044e-2 | 2.4385677685950416e-2 | 1.0922150082644576e-4 | 5.946503933885027e-4 | 4.8544156041322646e-5 | 1.8920694335086369e-3 | 1.6195673051840817e-3 | 7.424993755995493e-4 | 4.349792447357272e-2 | 4.0243848041459473e-2 | 2.7248841729503828e-2 |
| user_002 | Standing | 1.446034740236e12 | -1.45557 | -9.38788 | 0.229323 | -1.37893 | -9.422716363636363 | 0.22583945454545454 | 2.1186840249000003 | 88.13229089439999 | 5.2589038329e-2 | 9.502818737491998 | 9.526054521087183 | 7.664000000000004e-2 | 3.4836363636364e-2 | 3.4835454545454614e-3 | 3.8953388429751996e-2 | 3.0402644628099426e-2 | 4.592111570247934e-2 | 5.873689600000006e-3 | 1.213572231404984e-3 | 1.2135088933884345e-5 | 2.088447485499624e-3 | 1.2532891407964408e-3 | 2.9600787547911333e-3 | 4.569953484992625e-2 | 3.5401823975558674e-2 | 5.4406605801052624e-2 |
| user_002 | Standing | 1.446034741725e12 | -1.41725 | -9.38788 | 0.191003 | -1.3963481818181818 | -9.419232727272725 | 0.2188720909090909 | 2.0085975625 | 88.13229089439999 | 3.6482146009e-2 | 9.496176630776672 | 9.524973651272083 | 2.0901818181818133e-2 | 3.1352727272725645e-2 | 2.78690909090909e-2 | 3.990347107438013e-2 | 2.3752066115702967e-2 | 4.212066942148761e-2 | 4.3688600330578305e-4 | 9.829935074379145e-4 | 7.76686228099173e-4 | 2.33667816919609e-3 | 8.395713346356199e-4 | 2.8342982346739294e-3 | 4.83391990955176e-2 | 2.8975357368557508e-2 | 5.3238127640572876e-2 |
| user_002 | Standing | 1.446034742407e12 | -1.41725 | -9.34956 | 7.6042e-2 | -1.361511818181818 | -9.363494545454543 | 0.18403545454545453 | 2.0085975625 | 87.41427219360001 | 5.782385764e-3 | 9.456672360924006 | 9.464532622864683 | 5.573818181818191e-2 | 1.3934545454542757e-2 | 0.10799345454545453 | 7.220628099173551e-2 | 6.302214876033024e-2 | 7.125661157024793e-2 | 3.1067449123967045e-3 | 1.9417155702471822e-4 | 1.1662586224661153e-2 | 7.835263624943653e-3 | 5.865966867618297e-3 | 7.194372536848986e-3 | 8.85170244921487e-2 | 7.658960025759566e-2 | 8.481964711580087e-2 |
| user_002 | Standing | 1.446034742488e12 | -1.37893 | -9.38788 | 0.152682 | -1.330159090909091 | -9.440134545454544 | 0.19448627272727273 | 1.9014479449 | 88.13229089439999 | 2.3311793124000002e-2 | 9.489839336491634 | 9.536282590827007 | 4.8770909090908976e-2 | 5.225454545454511e-2 | 4.180427272727272e-2 | 7.790677685950409e-2 | 8.044033057851264e-2 | 6.33395123966942e-2 | 2.3786015735537077e-3 | 2.730537520661121e-3 | 1.7475972182561974e-3 | 9.165780089556715e-3 | 1.159733754229913e-2 | 7.4580968808392175e-3 | 9.57380806657242e-2 | 0.10769093528379782 | 8.636027374226657e-2 |
| user_002 | Standing | 1.446034742608e12 | -1.30229 | -9.31124 | 0.305964 | -1.6750418181818179 | -8.290524545454545 | 0.27809427272727266 | 1.6959592440999998 | 86.6991903376 | 9.361396929600001e-2 | 9.406846631629326 | 9.239319575440327 | 0.3727518181818179 | 1.0207154545454546 | 2.786972727272735e-2 | 0.3971372727272726 | 1.2180159504132235 | 9.500909917355373e-2 | 0.13894391795785102 | 1.041860039147934 | 7.767216982562027e-4 | 1.0335963619219386 | 12.500306028839441 | 1.8598867660080395e-2 | 1.0166594129411965 | 3.5355771846813697 | 0.13637766554711367 |
| user_002 | Standing | 1.446034742649e12 | -1.18733 | -9.46452 | 3.7722e-2 | -1.6576236363636363 | -8.26962272727273 | 0.2432577272727273 | 1.4097525289 | 89.5771388304 | 1.422949284e-3 | 9.538779497848978 | 9.215532139109781 | 0.4702936363636363 | 1.194897272727271 | 0.2055357272727273 | 0.5472515702479337 | 1.7076297520661157 | 0.12129477685950416 | 0.22117610440413218 | 1.4277794923710705 | 4.224493518552893e-2 | 1.1067117965771598 | 13.112466736772495 | 2.4742881798826447e-2 | 1.052003705590983 | 3.6211140187478903 | 0.15729870247025704 |
| user_002 | Standing | 1.446034742673e12 | -1.37893 | -9.27292 | 0.229323 | -1.6506563636363638 | -8.245237272727271 | 0.2397741818181818 | 1.9014479449 | 85.98704532639998 | 5.2589038329e-2 | 9.377690670395829 | 9.190260620809141 | 0.2717263636363638 | 1.0276827272727278 | 1.0451181818181804e-2 | 0.59729 | 1.986640082644628 | 0.11306066115702479 | 7.383521669504142e-2 | 1.0561317879347119 | 1.0922720139669392e-4 | 1.1242780968574755 | 13.443876168523282 | 2.3032821136954172e-2 | 1.0603198087640706 | 3.666589173676713 | 0.15176567838926616 |
| user_002 | Standing | 1.446034742722e12 | -1.45557 | -9.27292 | 0.152682 | -1.3545445454545453 | -9.41574909090909 | 0.19797018181818185 | 2.1186840249000003 | 85.98704532639998 | 2.3311793124000002e-2 | 9.387706916197585 | 9.515513032021708 | 0.10102545454545475 | 0.1428290909090908 | 4.528818181818184e-2 | 0.20458595041322322 | 0.6919823140495869 | 6.903980991735535e-2 | 1.0206142466115745e-2 | 2.0400149209917322e-2 | 2.051019412396696e-3 | 6.655770397761085e-2 | 0.7849239226030054 | 9.76386200915327e-3 | 0.25798779811768396 | 0.8859593233343196 | 9.88122563711267e-2 |
| user_002 | Standing | 1.446034742786e12 | -1.41725 | -9.54116 | 0.344284 | -1.4033154545454543 | -9.360010909090908 | 0.1770680909090909 | 2.0085975625 | 91.0337341456 | 0.11853147265599999 | 9.651987524896414 | 9.467182792168602 | 1.3934545454545644e-2 | 0.18114909090909137 | 0.16721590909090908 | 5.3521322314049644e-2 | 7.537322314049581e-2 | 9.595921487603305e-2 | 1.9417155702479866e-4 | 3.281499313719025e-2 | 2.796116025309917e-2 | 4.667840100976716e-3 | 9.441591960330594e-3 | 1.3405794349181821e-2 | 6.832159322627596e-2 | 9.716785456276471e-2 | 0.11578339409942093 |
| user_002 | Standing | 1.446034742868e12 | -1.30229 | -9.4262 | 0.267643 | -1.30229 | -9.461036363636362 | 0.21190472727272727 | 1.6959592440999998 | 88.85324643999999 | 7.163277544900001e-2 | 9.519497805007836 | 9.55316189549893 | 0.0 | 3.483636363636222e-2 | 5.573827272727275e-2 | 7.283966942148755e-2 | 8.455735537190069e-2 | 6.33393388429752e-2 | 0.0 | 1.2135722314048601e-3 | 3.106755046619837e-3 | 8.856870794289986e-3 | 1.0267924325168999e-2 | 5.675195743163036e-3 | 9.411094938576481e-2 | 0.1013307669228305 | 7.533389504839795e-2 |
| user_002 | Standing | 1.446034742989e12 | -1.34061 | -9.34956 | 0.229323 | -1.2326170909090908 | -9.422716363636363 | 0.17358436363636362 | 1.7972351721000002 | 87.41427219360001 | 5.2589038329e-2 | 9.447967845205074 | 9.506061671875052 | 0.1079929090909093 | 7.31563636363628e-2 | 5.573863636363638e-2 | 0.1326951157024794 | 6.428892561983429e-2 | 7.379039669421486e-2 | 1.1662468413917403e-2 | 5.351853540495745e-3 | 3.1067955836776876e-3 | 2.2827393172450052e-2 | 5.6673823206610735e-3 | 7.139237571364387e-3 | 0.15108736933460074 | 7.52820185745645e-2 | 8.449400908564102e-2 |
| user_002 | Standing | 1.446034743013e12 | -1.30229 | -9.27292 | 0.191003 | -1.2953226363636363 | -9.391363636363634 | 0.17358445454545454 | 1.6959592440999998 | 85.98704532639998 | 3.6482146009e-2 | 9.365868177404003 | 9.482930913178338 | 6.967363636363633e-3 | 0.11844363636363475 | 1.7418545454545464e-2 | 0.11084315702479342 | 5.9855206611569886e-2 | 6.1122528925619826e-2 | 4.854415604132226e-5 | 1.402889499504094e-2 | 3.0340572575206643e-4 | 1.5827245627852756e-2 | 4.978955891209522e-3 | 6.21910965048084e-3 | 0.1258063815068725 | 7.056171689527914e-2 | 7.886133178231801e-2 |
| user_002 | Standing | 1.446034743062e12 | -1.34061 | -9.69444 | 7.6042e-2 | -1.3127409090909092 | -9.468003636363637 | 0.14919881818181818 | 1.7972351721000002 | 93.9821669136 | 5.782385764e-3 | 9.78699057276873 | 9.56029440563364 | 2.7869090909090843e-2 | 0.22643636363636332 | 7.315681818181818e-2 | 7.030617355371901e-2 | 0.10577586776859524 | 5.922237190082646e-2 | 7.766862280991699e-4 | 5.1273426776859365e-2 | 5.351920046487603e-3 | 7.744808789910596e-3 | 1.5544757036814388e-2 | 5.9786087816852e-3 | 8.800459527723876e-2 | 0.12467861499396915 | 7.732146391323175e-2 |
| user_002 | Standing | 1.446034743119e12 | -1.34061 | -9.50284 | 0.305964 | -1.3406099999999999 | -9.44710181818182 | 0.22235572727272726 | 1.7972351721000002 | 90.30396806560002 | 9.361396929600001e-2 | 9.60181322495892 | 9.545211176307818 | 2.220446049250313e-16 | 5.573818181818169e-2 | 8.360827272727275e-2 | 7.885685950413222e-2 | 0.1285778512396694 | 7.347359504132234e-2 | 4.930380657631324e-32 | 3.1067449123966797e-3 | 6.9903432684380205e-3 | 9.559639441021782e-3 | 2.279750599068361e-2 | 6.198137410960935e-3 | 9.77734086601351e-2 | 0.15098842998946513 | 7.872825039946547e-2 |
| user_002 | Standing | 1.446034743127e12 | -1.30229 | -9.65612 | 0.114362 | -1.3440936363636362 | -9.461036363636364 | 0.20493736363636364 | 1.6959592440999998 | 93.24065345439999 | 1.3078667044000002e-2 | 9.744213224552508 | 9.559102111218815 | 4.1803636363636265e-2 | 0.1950836363636359 | 9.057536363636363e-2 | 7.632330578512397e-2 | 0.13902876033057848 | 6.935651239669423e-2 | 1.7475440132231322e-3 | 3.8057625176859324e-2 | 8.203896497859504e-3 | 9.277208085349355e-3 | 2.5673672179113358e-2 | 5.2658708311149535e-3 | 9.631826454701806e-2 | 0.16023006016073688 | 7.256632022581105e-2 |
| user_002 | Standing | 1.446034743142e12 | -1.18733 | -9.27292 | 0.114362 | -1.3440936363636362 | -9.443618181818183 | 0.20493727272727275 | 1.4097525289 | 85.98704532639998 | 1.3078667044000002e-2 | 9.349324923348423 | 9.541753629559041 | 0.15676363636363622 | 0.17069818181818341 | 9.057527272727274e-2 | 7.759008264462806e-2 | 0.14821289256198353 | 7.284019834710745e-2 | 2.4574837685950368e-2 | 2.9137869276033602e-2 | 8.203880029619836e-3 | 9.162470347107427e-3 | 2.6116074419834715e-2 | 5.6045778851179584e-3 | 9.572079370287015e-2 | 0.16160468563700348 | 7.486372876846276e-2 |
| user_002 | Standing | 1.446034743167e12 | -1.18733 | -9.31124 | 0.535886 | -1.3162245454545451 | -9.384396363636363 | 0.23629027272727277 | 1.4097525289 | 86.6991903376 | 0.287173804996 | 9.401920903278011 | 9.48030257103568 | 0.12889454545454515 | 7.31563636363628e-2 | 0.2995957272727272 | 7.442314049586768e-2 | 0.13269487603305777 | 8.804171900826446e-2 | 1.6613803847933806e-2 | 5.351853540495745e-3 | 8.975759980007435e-2 | 7.977582550262945e-3 | 2.1754937119158522e-2 | 1.2731709043015776e-2 | 8.931731383255402e-2 | 0.14749554948932703 | 0.11283487511853671 |
| user_002 | Standing | 1.446034743224e12 | -1.26397 | -9.69444 | 0.191003 | -1.26397 | -9.384396363636363 | 0.18403545454545453 | 1.5976201609 | 93.9821669136 | 3.6482146009e-2 | 9.778357184134205 | 9.472280420387362 | 0.0 | 0.31004363636363763 | 6.967545454545476e-3 | 7.663999999999994e-2 | 0.12002710743801646 | 9.025855371900825e-2 | 0.0 | 9.612705644958756e-2 | 4.8546689661157324e-5 | 1.2069527465063866e-2 | 2.0082413934785937e-2 | 1.4370043589776855e-2 | 0.10986140116102591 | 0.14171243394559963 | 0.11987511664134828 |
| user_002 | Standing | 1.446034743402e12 | -1.30229 | -9.50284 | 0.191003 | -1.2500354545454544 | -9.380912727272728 | 0.21538845454545452 | 1.6959592440999998 | 90.30396806560002 | 3.6482146009e-2 | 9.593560832960252 | 9.467103454007024 | 5.225454545454555e-2 | 0.1219272727272731 | 2.4385454545454516e-2 | 7.410644628099176e-2 | 7.949024793388433e-2 | 8.075765289256197e-2 | 2.7305375206611673e-3 | 1.4866259834710837e-2 | 5.946503933884283e-4 | 8.232435713062368e-3 | 9.36326138903078e-3 | 8.233667366213372e-3 | 9.073277088826488e-2 | 9.676394674170116e-2 | 9.073955789077535e-2 |
| user_002 | Standing | 1.446034743467e12 | -1.11069 | -9.34956 | 0.267643 | -1.204748181818182 | -9.443618181818183 | 0.281578 | 1.2336322760999998 | 87.41427219360001 | 7.163277544900001e-2 | 9.419104906791782 | 9.524990489698748 | 9.405818181818204e-2 | 9.405818181818226e-2 | 1.3934999999999975e-2 | 5.5421487603305675e-2 | 7.473983471074339e-2 | 9.374233057851239e-2 | 8.84694156694219e-3 | 8.846941566942232e-3 | 1.9418422499999931e-4 | 3.59548354740795e-3 | 9.260659373102885e-3 | 1.225509052579865e-2 | 5.996235108305836e-2 | 9.623231979487393e-2 | 0.11070271236875205 |
| user_002 | Standing | 1.446034743499e12 | -1.41725 | -9.31124 | 0.344284 | -1.236100909090909 | -9.454069090909089 | 0.271127 | 2.0085975625 | 86.6991903376 | 0.11853147265599999 | 9.4247715819937 | 9.539520739618162 | 0.18114909090909093 | 0.14282909090908902 | 7.315699999999997e-2 | 9.12079338842974e-2 | 6.840595041322234e-2 | 9.595928099173552e-2 | 3.2814993137190086e-2 | 2.0400149209916816e-2 | 5.351946648999996e-3 | 1.1244298347708476e-2 | 8.139759930277884e-3 | 1.2311364151235911e-2 | 0.10603913592494271 | 9.022061809962224e-2 | 0.11095658678616566 |
| user_002 | Standing | 1.446034743531e12 | -1.22565 | -9.54116 | 0.267643 | -1.2813881818181818 | -9.440134545454548 | 0.23977390909090912 | 1.5022179224999999 | 91.0337341456 | 7.163277544900001e-2 | 9.623283475173585 | 9.530636047867155 | 5.573818181818191e-2 | 0.10102545454545186 | 2.78690909090909e-2 | 8.899107438016535e-2 | 5.8905123966941565e-2 | 7.379038842975204e-2 | 3.1067449123967045e-3 | 1.020614246611516e-2 | 7.76686228099173e-4 | 1.2260389279639376e-2 | 5.87589609496609e-3 | 7.918149123489853e-3 | 0.11072664214017951 | 7.665439383992342e-2 | 8.898398239846232e-2 |
| user_002 | Standing | 1.446034743548e12 | -1.26397 | -9.34956 | 0.344284 | -1.2988063636363636 | -9.433167272727273 | 0.25719227272727274 | 1.5976201609 | 87.41427219360001 | 0.11853147265599999 | 9.440891050486496 | 9.526412109411659 | 3.4836363636363554e-2 | 8.360727272727253e-2 | 8.709172727272724e-2 | 8.772429752066122e-2 | 6.397223140495824e-2 | 6.777309917355369e-2 | 1.213572231404953e-3 | 6.990176052892529e-3 | 7.584968959347101e-3 | 1.203312029812172e-2 | 6.44075880631095e-3 | 6.451895272498118e-3 | 0.10969558012117772 | 8.025433823981698e-2 | 8.032369060556242e-2 |
| user_002 | Standing | 1.446034743589e12 | -1.14901 | -9.46452 | 0.305964 | -1.253519090909091 | -9.468003636363637 | 0.2641596363636364 | 1.3202239801000002 | 89.5771388304 | 9.361396929600001e-2 | 9.538919057199092 | 9.554780962492048 | 0.10450909090909088 | 3.4836363636365775e-3 | 4.180436363636364e-2 | 6.238876033057861e-2 | 7.378975206611571e-2 | 5.383840495867767e-2 | 1.0922150082644622e-2 | 1.2135722314051077e-5 | 1.7476048190413227e-3 | 6.439655558827958e-3 | 9.127166427648326e-3 | 4.695493552251689e-3 | 8.024746450092961e-2 | 9.553620480031812e-2 | 6.852367147381763e-2 |
| user_002 | Standing | 1.446034743669e12 | -1.18733 | -9.4262 | 0.382604 | -1.22565 | -9.433167272727273 | 0.23977418181818183 | 1.4097525289 | 88.85324643999999 | 0.146385820816 | 9.508384972734117 | 9.516067499453998 | 3.831999999999991e-2 | 6.967272727273155e-3 | 0.14282981818181817 | 5.890512396694219e-2 | 5.985520661157053e-2 | 6.080573553719005e-2 | 1.468422399999993e-3 | 4.854288925620431e-5 | 2.0400356961851236e-2 | 6.0921326016528995e-3 | 5.59677448174311e-3 | 5.5957542085063846e-3 | 7.805211465202529e-2 | 7.481159323088307e-2 | 7.480477396868722e-2 |
| user_002 | Standing | 1.446034743685e12 | -1.18733 | -9.4262 | 0.152682 | -1.2500354545454544 | -9.433167272727271 | 0.22583945454545457 | 1.4097525289 | 88.85324643999999 | 2.3311793124000002e-2 | 9.50191090055174 | 9.519018404063804 | 6.27054545454544e-2 | 6.967272727271379e-3 | 7.315745454545455e-2 | 6.618909090909093e-2 | 5.383801652892586e-2 | 6.112245454545453e-2 | 3.931974029752048e-3 | 4.8542889256179554e-5 | 5.3520131555702495e-3 | 7.812095427798655e-3 | 5.114655331630422e-3 | 5.658646282371899e-3 | 8.838605901271226e-2 | 7.151681852285113e-2 | 7.522397411977048e-2 |
| user_002 | Standing | 1.446034743742e12 | -1.18733 | -9.50284 | 0.191003 | -1.2813881818181818 | -9.419232727272727 | 0.26415954545454545 | 1.4097525289 | 90.30396806560002 | 3.6482146009e-2 | 9.578632613296588 | 9.510274138582078 | 9.405818181818182e-2 | 8.36072727272743e-2 | 7.315654545454545e-2 | 5.542148760330579e-2 | 8.20238016528926e-2 | 5.3838338842975196e-2 | 8.846941566942148e-3 | 6.990176052892826e-3 | 5.351880142842974e-3 | 5.27021322674681e-3 | 1.3544569349962465e-2 | 4.91833946714425e-3 | 7.25962342463217e-2 | 0.11638113829123027 | 7.013087385128072e-2 |
| user_002 | Standing | 1.446034743944e12 | -1.37893 | -9.38788 | -5.99e-4 | -1.2221663636363638 | -9.391363636363636 | 0.18055181818181823 | 1.9014479449 | 88.13229089439999 | 3.5880100000000004e-7 | 9.488611025756141 | 9.472946131104779 | 0.15676363636363622 | 3.4836363636365775e-3 | 0.18115081818181822 | 7.727335537190078e-2 | 6.0171900826446585e-2 | 7.284036363636363e-2 | 2.4574837685950368e-2 | 1.2135722314051077e-5 | 3.2815618927942165e-2 | 8.172852230090151e-3 | 5.555954324868545e-3 | 7.718468353941397e-3 | 9.040382862517578e-2 | 7.453827422786595e-2 | 8.785481406241434e-2 |
| user_002 | Standing | 1.446034743977e12 | -1.14901 | -9.4262 | 0.344284 | -1.2500354545454546 | -9.391363636363634 | 0.18403545454545453 | 1.3202239801000002 | 88.85324643999999 | 0.11853147265599999 | 9.502210368790832 | 9.476779695648814 | 0.10102545454545453 | 3.4836363636365775e-2 | 0.16024854545454545 | 7.188958677685946e-2 | 6.492231404958657e-2 | 9.310895867768593e-2 | 1.02061424661157e-2 | 1.2135722314051078e-3 | 2.567959632029752e-2 | 6.651479939298266e-3 | 6.682370005108915e-3 | 1.0884841720114202e-2 | 8.155660573674131e-2 | 8.174576444751688e-2 | 0.10433044483809222 |
| user_002 | Standing | 1.446034744098e12 | -1.22565 | -9.46452 | 0.305964 | -1.180362727272727 | -9.440134545454544 | 0.23977409090909088 | 1.5022179224999999 | 89.5771388304 | 9.361396929600001e-2 | 9.548453839349907 | 9.51709107277025 | 4.528727272727284e-2 | 2.4385454545456042e-2 | 6.618990909090913e-2 | 5.953851239669416e-2 | 6.270545454545468e-2 | 6.2389462809917355e-2 | 2.050937071074391e-3 | 5.946503933885027e-4 | 4.381104065462815e-3 | 4.190133940796387e-3 | 5.386054212471849e-3 | 4.8775717667821185e-3 | 6.473124393055016e-2 | 7.33897418749504e-2 | 6.98396145950285e-2 |
| user_002 | Standing | 1.446034744179e12 | -1.11069 | -9.50284 | 0.229323 | -1.1942972727272727 | -9.4262 | 0.2676434545454545 | 1.2336322760999998 | 90.30396806560002 | 5.2589038329e-2 | 9.570276348153643 | 9.505986245872176 | 8.360727272727275e-2 | 7.664000000000115e-2 | 3.832045454545452e-2 | 7.85401652892562e-2 | 5.637157024793444e-2 | 5.605547107438018e-2 | 6.990176052892566e-3 | 5.873689600000177e-3 | 1.4684572365702459e-3 | 8.300834062809922e-3 | 4.691008298121736e-3 | 4.023622975179566e-3 | 9.110891319080654e-2 | 6.849093588294539e-2 | 6.34320342979757e-2 |
| user_002 | Standing | 1.446034744195e12 | -1.26397 | -9.38788 | 7.6042e-2 | -1.2117154545454545 | -9.429683636363636 | 0.2502250909090909 | 1.5976201609 | 88.13229089439999 | 5.782385764e-3 | 9.47289255935398 | 9.511304498180628 | 5.225454545454555e-2 | 4.180363636363715e-2 | 0.1741830909090909 | 7.949024793388429e-2 | 5.3838016528926017e-2 | 6.42895785123967e-2 | 2.7305375206611673e-3 | 1.7475440132232066e-3 | 3.0339749158644624e-2 | 8.403436078737796e-3 | 4.293839204207379e-3 | 6.256626801931633e-3 | 9.167025732885119e-2 | 6.552739277742843e-2 | 7.909884197592043e-2 |
| user_002 | Standing | 1.446034744219e12 | -1.26397 | -9.4262 | 0.191003 | -1.2012645454545454 | -9.415749090909088 | 0.23629036363636358 | 1.5976201609 | 88.85324643999999 | 3.6482146009e-2 | 9.512483836880302 | 9.495637494109273 | 6.270545454545462e-2 | 1.0450909090911509e-2 | 4.528736363636357e-2 | 6.460561983471073e-2 | 5.16211570247943e-2 | 6.0172413223140486e-2 | 3.931974029752075e-3 | 1.0922150082649681e-4 | 2.0509453051322252e-3 | 5.7677778416228434e-3 | 4.504559473478679e-3 | 5.913502439409467e-3 | 7.594588758861696e-2 | 6.711601502978763e-2 | 7.689930064317534e-2 |
| user_002 | Standing | 1.446034744228e12 | -1.57053 | -9.4262 | 0.267643 | -1.2465518181818183 | -9.422716363636361 | 0.23280663636363635 | 2.4665644809 | 88.85324643999999 | 7.163277544900001e-2 | 9.559887221947182 | 9.508733561792187 | 0.3239781818181817 | 3.483636363638354e-3 | 3.4836363636363665e-2 | 8.265719008264463e-2 | 4.75041322314061e-2 | 5.953897520661156e-2 | 0.10496186229421481 | 1.2135722314063454e-5 | 1.2135722314049607e-3 | 1.3879956584823444e-2 | 4.289426214275079e-3 | 5.864955404311043e-3 | 0.11781322754607584 | 6.549371125745646e-2 | 7.65829968355316e-2 |
| user_002 | Standing | 1.446034744503e12 | -1.26397 | -9.46452 | 0.152682 | -1.243068181818182 | -9.447101818181817 | 0.26764327272727273 | 1.5976201609 | 89.5771388304 | 2.3311793124000002e-2 | 9.54976810108099 | 9.532705592206456 | 2.0901818181818133e-2 | 1.7418181818182887e-2 | 0.11496127272727272 | 5.7004958677685895e-2 | 5.003768595041403e-2 | 5.890569421487604e-2 | 4.3688600330578305e-4 | 3.0339305785127694e-4 | 1.3216094227074378e-2 | 4.053331252892559e-3 | 3.6164452495868597e-3 | 4.927201550427498e-3 | 6.366577772157157e-2 | 6.013688759477713e-2 | 7.019402788291536e-2 |
| user_002 | Standing | 1.446034744527e12 | -1.30229 | -9.34956 | 0.191003 | -1.2570027272727275 | -9.419232727272728 | 0.22235572727272726 | 1.6959592440999998 | 87.41427219360001 | 3.6482146009e-2 | 9.441753734540475 | 9.505965099520607 | 4.52872727272724e-2 | 6.9672727272728e-2 | 3.1352727272727254e-2 | 4.782082644628092e-2 | 6.112198347107474e-2 | 8.044102479338842e-2 | 2.0509370710743505e-3 | 4.854288925619936e-3 | 9.829935074380154e-4 | 2.7812869048835388e-3 | 5.221670337490657e-3 | 9.563103558231404e-3 | 5.273790766501397e-2 | 7.226112604637888e-2 | 9.779112208289362e-2 |
| user_002 | Standing | 1.446034744568e12 | -1.26397 | -9.50284 | 0.344284 | -1.30229 | -9.429683636363634 | 0.2293229090909091 | 1.5976201609 | 90.30396806560002 | 0.11853147265599999 | 9.59271180111005 | 9.523035990951529 | 3.831999999999991e-2 | 7.315636363636635e-2 | 0.11496109090909087 | 5.668826446280986e-2 | 5.985520661157102e-2 | 0.10894367768595038 | 1.468422399999993e-3 | 5.351853540496265e-3 | 1.3216052423008256e-2 | 5.508514683095412e-3 | 5.303310651239775e-3 | 1.5701674011886546e-2 | 7.421936865195912e-2 | 7.282383298920604e-2 | 0.12530632071801703 |
| user_002 | Standing | 1.44603474464e12 | -1.53221 | -9.34956 | 0.114362 | -1.229133636363636 | -9.481938181818181 | 0.239774 | 2.3476674841 | 87.41427219360001 | 1.3078667044000002e-2 | 9.474967986475944 | 9.566116910347588 | 0.30307636363636403 | 0.13237818181818106 | 0.12541199999999997 | 0.12224396694214877 | 6.967272727272751e-2 | 0.11052728925619833 | 9.185528219504156e-2 | 1.7523983021487402e-2 | 1.572816974399999e-2 | 2.093301774425246e-2 | 7.592549178662661e-3 | 1.564765070072727e-2 | 0.14468247213899982 | 8.71352350009034e-2 | 0.12509056999121584 |
| user_002 | Standing | 1.446034744786e12 | -1.14901 | -9.38788 | 7.6042e-2 | -1.257002727272727 | -9.433167272727273 | 0.2537086363636364 | 1.3202239801000002 | 88.13229089439999 | 5.782385764e-3 | 9.458239649124142 | 9.520822783672006 | 0.10799272727272702 | 4.528727272727373e-2 | 0.1776666363636364 | 8.107371900826443e-2 | 7.315636363636457e-2 | 8.772486776859503e-2 | 1.1662429143801598e-2 | 2.050937071074471e-3 | 3.15654336767686e-2 | 8.854664299323807e-3 | 7.567174486551606e-3 | 1.0089337290975957e-2 | 9.409922581681428e-2 | 8.698950791073373e-2 | 0.10044569324254753 |
| user_002 | Standing | 1.446034744924e12 | -1.41725 | -9.19628 | 0.344284 | -1.2395845454545453 | -9.454069090909092 | 0.26415972727272724 | 2.0085975625 | 84.57156583839999 | 0.11853147265599999 | 9.311213394265861 | 9.54034444189835 | 0.17766545454545457 | 0.2577890909090925 | 8.012427272727274e-2 | 0.11179305785123973 | 9.342479338843016e-2 | 8.772514876033057e-2 | 3.156501373884298e-2 | 6.645521539173636e-2 | 6.419899080074382e-3 | 1.7350773166641645e-2 | 1.2785535081592858e-2 | 1.0413754814553715e-2 | 0.1317223335909353 | 0.11307314040740558 | 0.1020478065151511 |
| user_002 | Standing | 1.44603474494e12 | -1.26397 | -9.4262 | 0.344284 | -1.246551818181818 | -9.436650909090908 | 0.25719245454545453 | 1.5976201609 | 88.85324643999999 | 0.11853147265599999 | 9.516795578006075 | 9.523606955107557 | 1.7418181818182e-2 | 1.0450909090907956e-2 | 8.709154545454545e-2 | 9.595834710743807e-2 | 8.01236363636366e-2 | 9.405902479338843e-2 | 3.03393057851246e-4 | 1.0922150082642256e-4 | 7.584937289661156e-3 | 1.3732121422088666e-2 | 1.0861471471074448e-2 | 1.093449079350563e-2 | 0.11718413468592354 | 0.10421838355623468 | 0.10456811556830137 |
| user_002 | Standing | 1.446034744989e12 | -1.41725 | -9.19628 | 0.191003 | -1.34061 | -9.412265454545455 | 0.2711271818181818 | 2.0085975625 | 84.57156583839999 | 3.6482146009e-2 | 9.306806409661103 | 9.512400694854856 | 7.663999999999982e-2 | 0.21598545454545537 | 8.012418181818179e-2 | 8.265719008264466e-2 | 9.08912396694219e-2 | 0.11242739669421485 | 5.873689599999972e-3 | 4.6649716575206966e-2 | 6.419884512033053e-3 | 1.154327841562735e-2 | 1.4186659385124128e-2 | 1.8352836216098418e-2 | 0.1074396501093863 | 0.11910776374831376 | 0.13547264010160287 |
| user_002 | Standing | 1.446034745069e12 | -1.14901 | -9.4262 | 0.382604 | -1.3162245454545454 | -9.440134545454546 | 0.3164147272727273 | 1.3202239801000002 | 88.85324643999999 | 0.146385820816 | 9.50367593307537 | 9.537335764246192 | 0.16721454545454528 | 1.393454545454631e-2 | 6.618927272727271e-2 | 6.460561983471075e-2 | 9.722512396694262e-2 | 6.270590909090909e-2 | 2.796070421157019e-2 | 1.9417155702481723e-4 | 4.381019824165286e-3 | 6.482682210668667e-3 | 1.4374211457250293e-2 | 5.569287497270473e-3 | 8.051510548132361e-2 | 0.11989249958713136 | 7.462765906331562e-2 |
| user_002 | Standing | 1.446034745312e12 | -1.34061 | -9.34956 | 0.420925 | -1.246551818181818 | -9.41574909090909 | 0.28157790909090913 | 1.7972351721000002 | 87.41427219360001 | 0.177177855625 | 9.454558964929301 | 9.502762210177798 | 9.405818181818204e-2 | 6.618909090908964e-2 | 0.13934709090909086 | 7.600664462809915e-2 | 8.867438016528904e-2 | 5.1938297520661154e-2 | 8.84694156694219e-3 | 4.380995755371733e-3 | 1.9417611744826434e-2 | 8.091224411377905e-3 | 1.2680726570698658e-2 | 4.6888999789346355e-3 | 8.99512335178229e-2 | 0.11260873221335306 | 6.847554292544628e-2 |
| user_002 | Standing | 1.446034745417e12 | -1.03405 | -9.31124 | 0.382604 | -1.2570027272727275 | -9.4262 | 0.22235563636363634 | 1.0692594024999997 | 86.6991903376 | 0.146385820816 | 9.376291141006448 | 9.513699984452389 | 0.22295272727272764 | 0.11495999999999995 | 0.16024836363636366 | 0.11147636363636365 | 6.397223140495889e-2 | 0.10799378512396694 | 4.970791859834727e-2 | 1.3215801599999988e-2 | 2.567953804813224e-2 | 1.5727896119008274e-2 | 5.2051216252441725e-3 | 1.6874486719385424e-2 | 0.12541090909090913 | 7.214652885097225e-2 | 0.12990183493463603 |
| user_002 | Standing | 1.44603474545e12 | -1.18733 | -9.38788 | 0.152682 | -1.2012645454545454 | -9.40529818181818 | 0.20842090909090907 | 1.4097525289 | 88.13229089439999 | 2.3311793124000002e-2 | 9.463897464386646 | 9.48483192792895 | 1.3934545454545422e-2 | 1.741818181818111e-2 | 5.573890909090906e-2 | 8.26571900826447e-2 | 4.4653884297521144e-2 | 9.722603305785121e-2 | 1.9417155702479248e-4 | 3.0339305785121503e-4 | 3.1068259866446246e-3 | 1.1960305964237434e-2 | 3.1343260994741193e-3 | 1.4745169326966191e-2 | 0.10936318376966461 | 5.5985052464690245e-2 | 0.12142968882018182 |
| user_002 | Standing | 1.446034745458e12 | -1.11069 | -9.31124 | 0.114362 | -1.197780909090909 | -9.391363636363634 | 0.19100254545454545 | 1.2336322760999998 | 86.6991903376 | 1.3078667044000002e-2 | 9.377947604926357 | 9.470198159540521 | 8.709090909090911e-2 | 8.012363636363418e-2 | 7.664054545454545e-2 | 7.568991735537196e-2 | 4.877090909090934e-2 | 0.10387662809917354 | 7.584826446280995e-3 | 6.419797104131881e-3 | 5.873773207570247e-3 | 1.0212761951014294e-2 | 3.6076192697220193e-3 | 1.5278045231654396e-2 | 0.1010582107055844 | 6.0063460354212186e-2 | 0.1236043900177271 |
| user_002 | Standing | 1.446034745523e12 | -1.18733 | -9.46452 | 0.267643 | -1.2639699999999998 | -9.433167272727273 | 0.20493736363636364 | 1.4097525289 | 89.5771388304 | 7.163277544900001e-2 | 9.542459019285804 | 9.520695509724966 | 7.663999999999982e-2 | 3.135272727272742e-2 | 6.270563636363638e-2 | 8.329057851239673e-2 | 7.220628099173529e-2 | 7.569057851239669e-2 | 5.873689599999972e-3 | 9.829935074380258e-4 | 3.931996831768597e-3 | 1.132483541397447e-2 | 9.218735968745291e-3 | 8.224858660748308e-3 | 0.10641820997354949 | 9.601424877977899e-2 | 9.069100650421909e-2 |
| user_002 | Standing | 1.446034745563e12 | -1.18733 | -9.31124 | 0.267643 | -1.312740909090909 | -9.44361818181818 | 0.23280663636363635 | 1.4097525289 | 86.6991903376 | 7.163277544900001e-2 | 9.390451301292659 | 9.538040096804464 | 0.12541090909090902 | 0.13237818181818106 | 3.4836363636363665e-2 | 9.659173553719014e-2 | 8.265719008264485e-2 | 5.41550909090909e-2 | 1.5727896119008246e-2 | 1.7523983021487402e-2 | 1.2135722314049607e-3 | 1.1587408314951177e-2 | 1.0879123430803916e-2 | 5.431383807558227e-3 | 0.10764482484054297 | 0.10430303653683298 | 7.36979226814313e-2 |
| user_002 | Standing | 1.446034745571e12 | -1.41725 | -9.34956 | 0.459245 | -1.312740909090909 | -9.4262 | 0.2537086363636364 | 2.0085975625 | 87.41427219360001 | 0.210905970025 | 9.467511591021424 | 9.521580158050632 | 0.10450909090909088 | 7.663999999999938e-2 | 0.20553636363636363 | 8.86743801652893e-2 | 7.695669421487607e-2 | 7.062333057851237e-2 | 1.0922150082644622e-2 | 5.8736895999999044e-3 | 4.22451967768595e-2 | 9.24300741337341e-3 | 9.647899239669385e-3 | 9.21779590594365e-3 | 9.614056070864893e-2 | 9.822372035139672e-2 | 9.600935322115055e-2 |
| user_002 | Standing | 1.44603474566e12 | -1.14901 | -9.69444 | 0.191003 | -1.1942972727272727 | -9.41574909090909 | 0.2467412727272727 | 1.3202239801000002 | 93.9821669136 | 3.6482146009e-2 | 9.764162690149575 | 9.495286842248932 | 4.528727272727262e-2 | 0.2786909090909102 | 5.573827272727269e-2 | 8.360727272727261e-2 | 8.582413223140523e-2 | 8.487476033057852e-2 | 2.0509370710743704e-3 | 7.766862280991797e-2 | 3.106755046619831e-3 | 1.089125915311792e-2 | 1.2984119628550029e-2 | 8.934242761645379e-3 | 0.10436119562901681 | 0.11394788119377222 | 9.452112336216376e-2 |
| user_002 | Standing | 1.446034746211e12 | -1.22565 | -9.34956 | 0.229323 | -1.2291336363636363 | -9.433167272727271 | 0.250225 | 1.5022179224999999 | 87.41427219360001 | 5.2589038329e-2 | 9.432342188153958 | 9.51676625630396 | 3.4836363636363554e-3 | 8.360727272727075e-2 | 2.0901999999999976e-2 | 8.360727272727274e-2 | 6.1121983471074254e-2 | 6.460625619834709e-2 | 1.213572231404953e-5 | 6.990176052892232e-3 | 4.36893603999999e-4 | 1.064633821187077e-2 | 7.6267498506385895e-3 | 6.597537471873022e-3 | 0.10318109425602526 | 8.733126502369348e-2 | 8.122522681946183e-2 |
| user_002 | Standing | 1.446034748024e12 | -1.26397 | -9.34956 | 0.305964 | -1.22565 | -9.426199999999996 | 0.2711270909090909 | 1.5976201609 | 87.41427219360001 | 9.361396929600001e-2 | 9.43957129978878 | 9.509700619608166 | 3.832000000000013e-2 | 7.663999999999582e-2 | 3.483690909090914e-2 | 3.515305785123967e-2 | 3.641983471074394e-2 | 4.908799173553719e-2 | 1.4684224000000102e-3 | 5.87368959999936e-3 | 1.213610235008268e-3 | 1.6405290073628856e-3 | 2.1656748093162987e-3 | 3.4432827263538705e-3 | 4.0503444388877416e-2 | 4.653681133593382e-2 | 5.86794915311463e-2 |
| user_002 | Standing | 1.446034748412e12 | -1.26397 | -9.4262 | 0.191003 | -1.2361009090909092 | -9.419232727272725 | 0.27461081818181815 | 1.5976201609 | 88.85324643999999 | 3.6482146009e-2 | 9.512483836880302 | 9.50422673863462 | 2.7869090909090843e-2 | 6.967272727274931e-3 | 8.360781818181814e-2 | 4.370380165289258e-2 | 2.3118677685950703e-2 | 4.243754545454546e-2 | 7.766862280991699e-4 | 4.854288925622906e-5 | 6.990267261123961e-3 | 2.367569098722766e-3 | 9.984389722013142e-4 | 2.4360166227340345e-3 | 4.86576725576015e-2 | 3.159808494515631e-2 | 4.935601911351881e-2 |
| user_002 | Standing | 1.446034749448e12 | -1.26397 | -9.46452 | 0.267643 | -1.2430681818181817 | -9.44361818181818 | 0.27809436363636364 | 1.5976201609 | 89.5771388304 | 7.163277544900001e-2 | 9.552297721844154 | 9.529247951373268 | 2.0901818181818355e-2 | 2.0901818181819465e-2 | 1.045136363636362e-2 | 2.3435371900826493e-2 | 2.565223140495815e-2 | 2.311914876033059e-2 | 4.3688600330579237e-4 | 4.368860033058388e-4 | 1.0923100185950379e-4 | 1.1562033622839983e-3 | 9.212116483846524e-4 | 7.40301517880542e-4 | 3.4002990490308325e-2 | 3.0351468636371656e-2 | 2.7208482461918784e-2 |
| user_002 | Standing | 1.446034749902e12 | -1.26397 | -9.4262 | 0.305964 | -1.2500354545454544 | -9.436650909090908 | 0.29899645454545454 | 1.5976201609 | 88.85324643999999 | 9.361396929600001e-2 | 9.51548635489516 | 9.52386574249788 | 1.3934545454545644e-2 | 1.0450909090907956e-2 | 6.967545454545476e-3 | 2.9135867768595072e-2 | 1.4567933884297285e-2 | 1.995216528925621e-2 | 1.9417155702479866e-4 | 1.0922150082642256e-4 | 4.8546689661157324e-5 | 1.125312432757327e-3 | 2.4050795131480226e-4 | 5.461251240390691e-4 | 3.354567681173428e-2 | 1.5508318777830248e-2 | 2.336932014499072e-2 |
| user_002 | Standing | 1.446146629807e12 | -3.83143 | -8.69811 | -1.30349 | -1.9432845454545455 | -9.224146363636363 | -0.1260110909090909 | 14.6798558449 | 75.65711757209999 | 1.6990861801000001 | 9.593542598909956 | 9.525586852701506 | 1.8881454545454546 | 0.5260363636363632 | 1.177478909090909 | 0.5865221487603306 | 0.1710171074380165 | 0.37306916528925615 | 3.5650932575206613 | 0.2767142558677681 | 1.3864565813539171 | 1.2136749636270474 | 9.244811132719769e-2 | 0.42548822749008636 | 1.1016691715878444 | 0.3040528100958741 | 0.6522945864332207 |
| user_002 | Standing | 1.446146630131e12 | -4.09967 | -8.73643 | -0.958607 | -3.1974027272727272 | -8.907130909090908 | -0.6694627272727273 | 16.8072941089 | 76.3252091449 | 0.918927380449 | 9.698011684579937 | 9.587718584746405 | 0.9022672727272725 | 0.17070090909090752 | 0.2891442727272727 | 1.1758942148760327 | 0.3018138842975207 | 0.567837438016529 | 0.8140862314347103 | 2.9138800364462272e-2 | 8.360441045098346e-2 | 2.0436872127891808 | 0.13667532388369658 | 0.5365504326603726 | 1.4295758856350302 | 0.3696962589528011 | 0.7324960291089452 |
| user_002 | Standing | 1.446146631166e12 | -4.09967 | -8.62147 | -1.03525 | -3.9707763636363635 | -8.691142727272727 | -0.996927909090909 | 16.8072941089 | 74.3297449609 | 1.0717425625 | 9.602540373895858 | 9.616297935504827 | 0.12889363636363615 | 6.967272727272622e-2 | 3.8322090909091e-2 | 0.21313595041322309 | 8.994140495867792e-2 | 0.20236987603305776 | 1.661356949504127e-2 | 4.854288925619688e-3 | 1.4685826516446351e-3 | 9.500073981960924e-2 | 1.3845801978136778e-2 | 5.8809805061572476e-2 | 0.3082219002919962 | 0.11766818592184031 | 0.24250732991308216 |
| user_002 | Standing | 1.446146632203e12 | -3.10335 | -9.38788 | -1.11189 | -3.7338900000000006 | -8.816554545454544 | -0.9829934545454548 | 9.6307812225 | 88.13229089439999 | 1.2362993721000002 | 9.949842787149956 | 9.633812047910272 | 0.6305400000000008 | 0.5713254545454554 | 0.1288965454545452 | 0.27267628099173585 | 0.1789328925619839 | 7.98085537190083e-2 | 0.397580691600001 | 0.3264127750115712 | 1.661431943011564e-2 | 0.11091659914207376 | 6.172432608730298e-2 | 1.119388742651014e-2 | 0.33304143757507676 | 0.24844380871195598 | 0.10580116930596815 |
| user_002 | Standing | 1.446146632332e12 | -3.71647 | -8.96635 | -1.49509 | -3.6746672727272727 | -8.833972727272727 | -1.0491828181818181 | 13.812149260900002 | 80.3954323225 | 2.2352941081 | 9.820533371029295 | 9.633098330877985 | 4.180272727272749e-2 | 0.13237727272727362 | 0.4459071818181819 | 0.2568404958677688 | 0.1450466115702482 | 0.12889628099173558 | 1.7474680074380348e-3 | 1.752374233471098e-2 | 0.19883321479703311 | 0.10328141370202867 | 4.936462438091678e-2 | 3.491716672898422e-2 | 0.3213742579952985 | 0.22218151223924276 | 0.1868613569708414 |
| user_002 | Standing | 1.446146632591e12 | -4.75112 | -7.97003 | -0.920286 | -4.078772727272727 | -8.628437272727274 | -1.0979536363636366 | 22.573141254400003 | 63.5213782009 | 0.8469263217960001 | 9.324239688955663 | 9.645194785304184 | 0.672347272727273 | 0.6584072727272732 | 0.17766763636363658 | 0.49183024793388447 | 0.3455149586776864 | 0.16658295041322313 | 0.45205085514380206 | 0.43350013678016586 | 3.15657890110414e-2 | 0.44089569586581534 | 0.2401217145223895 | 4.4869705995755814e-2 | 0.6639997709832552 | 0.49002215717494807 | 0.21182470582006202 |
| user_002 | Standing | 1.44614663285e12 | -4.44456 | -9.15796 | -0.881966 | -4.664029090909091 | -8.384581818181818 | -1.3557447272727272 | 19.7541135936 | 83.86823136159998 | 0.7778640251560001 | 10.217642046008265 | 9.751354221095463 | 0.21946909090909106 | 0.7733781818181811 | 0.4737787272727272 | 0.7730566115702483 | 0.5374344628099171 | 0.32873110743801653 | 4.816668186446287e-2 | 0.5981138121123956 | 0.2244662824161652 | 0.8926250418875287 | 0.4177421818151013 | 0.1757677490628174 | 0.9447883582514809 | 0.6463297779114786 | 0.41924664466494826 |
| user_002 | Standing | 1.446146633563e12 | 0.307161 | -8.42987 | 3.83143 | -3.9220080909090913 | -8.68417909090909 | -8.420636363636369e-2 | 9.434787992100001e-2 | 71.06270821689999 | 14.6798558449 | 9.264821203980194 | 9.868962821249822 | 4.2291690909090915 | 0.25430909090909104 | 3.915636363636364 | 1.309858099173554 | 0.6036228099173553 | 1.1698770247933885 | 17.885871199500833 | 6.467311371900833e-2 | 15.332208132231406 | 3.174886053125245 | 0.5472165834318563 | 2.9135858326633977 | 1.7818209935695688 | 0.7397408893875317 | 1.706922913509394 |
| user_002 | Standing | 1.446146633821e12 | 0.805325 | -9.69444 | -2.03158 | -2.3961624545454545 | -9.231113636363636 | 6.907500000000001e-2 | 0.6485483556249999 | 93.9821669136 | 4.127317296399999 | 9.937707611196105 | 9.916000812201998 | 3.2014874545454544 | 0.46332636363636404 | 2.100655 | 1.859644537190083 | 0.7474038016528933 | 1.4048668264462811 | 10.249521921611933 | 0.21467131924049623 | 4.4127514290250005 | 5.011452262465596 | 0.8069646739427511 | 3.38240286656267 | 2.23862731656379 | 0.8983121250115413 | 1.8391310085370944 |
| user_002 | Standing | 1.44614663531e12 | -1.68549 | -8.92803 | 1.26397 | -6.367999999999808e-3 | -9.708374545454546 | 1.121141 | 2.8408765400999996 | 79.7097196809 | 1.5976201609 | 9.173233692755243 | 9.850388442713218 | 1.6791220000000002 | 0.7803445454545468 | 0.1428290000000001 | 0.9054352396694214 | 0.7879416528925623 | 0.5947563636363636 | 2.819450690884001 | 0.6089376096206632 | 2.0400123241000028e-2 | 1.0586213222613254 | 1.0029801162580023 | 0.6618157154490096 | 1.028893251149664 | 1.0014889496434807 | 0.813520568546985 |
| user_002 | Standing | 1.446146636087e12 | -1.41725 | -9.04299 | 1.41725 | -1.434669 | -9.144022727272727 | 1.5914354545454545 | 2.0085975625 | 81.7756681401 | 2.0085975625 | 9.262443698349804 | 9.402852673801274 | 1.7419000000000073e-2 | 0.10103272727272739 | 0.17418545454545464 | 0.4547756280991736 | 0.2530417355371902 | 0.3363332148760331 | 3.0342156100000254e-4 | 1.0207611980165312e-2 | 3.0340572575206646e-2 | 0.30754998365910374 | 0.10993550811472588 | 0.27250323172102253 | 0.5545718922367989 | 0.33156523960561046 | 0.5220184208636919 |
| user_002 | Standing | 1.446146636411e12 | -1.26397 | -9.2346 | 1.60885 | -1.298806272727273 | -9.185827272727273 | 1.5147927272727273 | 1.5976201609 | 85.27783716 | 2.5883983225 | 9.458533482702274 | 9.40201223008114 | 3.4836272727272855e-2 | 4.877272727272697e-2 | 9.40572727272726e-2 | 0.15739753719008268 | 0.11496322314049562 | 9.722600826446276e-2 | 1.2135658975289344e-3 | 2.378778925619805e-3 | 8.846770552892538e-3 | 5.141716259284153e-2 | 2.2442123278512357e-2 | 1.2644633745004498e-2 | 0.22675352829193535 | 0.14980695337170555 | 0.11244836034822606 |
| user_002 | Standing | 1.446146638612e12 | 0.843646 | -10.1926 | 2.49022 | 0.20961881818181816 | -9.109183636363637 | 1.9223843636363638 | 0.711738573316 | 103.88909476 | 6.2011956484 | 10.526254271188588 | 9.36654512772446 | 0.6340271818181819 | 1.0834163636363634 | 0.5678356363636361 | 0.7971253140495868 | 0.7556363636363634 | 0.6384612727272727 | 0.4019904672843058 | 1.1737910169950407 | 0.32243730992449554 | 0.772237746696704 | 0.7814514966276483 | 0.5705717780425935 | 0.8787705882064465 | 0.8839974528400228 | 0.7553620178712943 |
| user_002 | Standing | 1.446146639259e12 | -0.497565 | -9.19628 | 1.76214 | -0.23977409090909088 | -9.366977272727274 | 1.598401818181818 | 0.24757092922499999 | 84.57156583839999 | 3.1051373796000004 | 9.376794449449395 | 9.519829806157274 | 0.25779090909090907 | 0.1706972727272742 | 0.163738181818182 | 0.5260335371900827 | 0.38922008264462815 | 0.3996728760330578 | 6.645615280991735e-2 | 2.9137558916529426e-2 | 2.6810192185124027e-2 | 0.3264686576048926 | 0.21898335570706234 | 0.2096612856533523 | 0.5713743585469098 | 0.46795657459540235 | 0.45788785270342375 |
| user_002 | Standing | 1.446146639583e12 | -0.344284 | -9.38788 | 2.03038 | -0.396539 | -9.20673 | 1.709881818181818 | 0.11853147265599999 | 88.13229089439999 | 4.1224429444 | 9.611101149787988 | 9.376979023936917 | 5.2254999999999996e-2 | 0.1811499999999988 | 0.320498181818182 | 0.4113893884297521 | 0.14061272727272775 | 0.28787975206611577 | 2.7305850249999997e-3 | 3.281532249999957e-2 | 0.10271908454876046 | 0.24823389758578668 | 3.009893257986488e-2 | 0.1276876912033809 | 0.49823076740180017 | 0.17349043944801362 | 0.35733414502868444 |
| user_002 | Standing | 1.446146640101e12 | -0.191003 | -9.15796 | 2.14534 | -0.177068 | -9.192796363636365 | 1.9223872727272728 | 3.6482146009e-2 | 83.86823136159998 | 4.6024837156 | 9.407826381434182 | 9.394355999014406 | 1.3935000000000003e-2 | 3.4836363636365775e-2 | 0.2229527272727272 | 0.12731222314049587 | 6.080545454545539e-2 | 0.12604595041322328 | 1.9418422500000007e-4 | 1.2135722314051078e-3 | 4.970791859834707e-2 | 2.599075022934561e-2 | 6.286337555822744e-3 | 2.371156496318561e-2 | 0.1612164700933053 | 7.928642731150612e-2 | 0.15398559985656324 |
| user_002 | Standing | 1.446146641202e12 | -7.6042e-2 | -9.00468 | 2.10702 | -0.1666170909090909 | -9.112672727272729 | 2.10702 | 5.782385764e-3 | 81.0842619024 | 4.439533280399999 | 9.248220237892477 | 9.35506650966221 | 9.057509090909091e-2 | 0.10799272727272857 | 0.0 | 7.569043801652892e-2 | 4.655404958677794e-2 | 3.45196694214877e-2 | 8.203847093190083e-3 | 1.1662429143801934e-2 | 0.0 | 7.256156008209616e-3 | 3.9573487218633316e-3 | 1.6846589066867126e-3 | 8.518307348417065e-2 | 6.290746157542308e-2 | 4.1044596558946864e-2 |
| user_002 | Standing | 1.446146641525e12 | -0.267643 | -9.08132 | 2.14534 | -0.14223136363636363 | -9.119640000000002 | 2.127921818181818 | 7.163277544900001e-2 | 82.47037294239999 | 4.6024837156 | 9.335121286488409 | 9.366200339151453 | 0.1254116363636364 | 3.832000000000235e-2 | 1.7418181818182e-2 | 9.722576859504133e-2 | 5.4788099173553366e-2 | 3.8320000000000014e-2 | 1.5728078535404966e-2 | 1.4684224000001804e-3 | 3.03393057851246e-4 | 1.0720399860847486e-2 | 4.665633606010519e-3 | 2.0487305761081945e-3 | 0.10353936382288374 | 6.830544345812066e-2 | 4.5262905078090096e-2 |
| user_002 | Standing | 1.446146642043e12 | -7.6042e-2 | -9.15796 | 2.22198 | -0.17358436363636362 | -9.137057272727274 | 2.194110909090909 | 5.782385764e-3 | 83.86823136159998 | 4.937195120399999 | 9.423969910168642 | 9.398903411353272 | 9.754236363636362e-2 | 2.090272727272513e-2 | 2.7869090909090843e-2 | 7.062344628099174e-2 | 3.515396694214814e-2 | 5.2254545454545394e-2 | 9.514512703768591e-3 | 4.3692400743792695e-4 | 7.766862280991699e-4 | 6.6537959533966955e-3 | 2.3644125287002114e-3 | 4.283909976859498e-3 | 8.15708033146462e-2 | 4.862522523032887e-2 | 6.545158498355481e-2 |
| user_002 | Standing | 1.446146644761e12 | 3.56439 | -8.58315 | 1.18733 | 3.867466363636363 | -8.33232818181818 | 1.90845 | 12.7048760721 | 73.67046392249999 | 1.4097525289 | 9.36936991069837 | 9.391053458142947 | 0.30307636363636314 | 0.25082181818181937 | 0.72112 | 0.24797165289256215 | 0.1133766115702486 | 0.3094122314049587 | 9.185528219504102e-2 | 6.291158447603365e-2 | 0.5200140543999999 | 7.199465958354628e-2 | 1.7020939596919755e-2 | 0.14594144823771596 | 0.2683182058369247 | 0.1304643230807555 | 0.38202283732483316 |
| user_002 | Standing | 1.446146645861e12 | 3.90927 | -8.50651 | 1.30229 | 3.6445127272727276 | -8.499542727272727 | 1.295322727272727 | 15.282391932899998 | 72.36071238010001 | 1.6959592440999998 | 9.45193438176017 | 9.340642711649064 | 0.26475727272727223 | 6.967272727273155e-3 | 6.967272727272933e-3 | 0.16563198347107458 | 3.3569586776859464e-2 | 8.075710743801658e-2 | 7.009641346198321e-2 | 4.854288925620431e-5 | 4.854288925620121e-5 | 5.16384289590534e-2 | 1.921857115552206e-3 | 1.162056965447032e-2 | 0.2272409051184522 | 4.383899081356921e-2 | 0.1077987460709554 |
| user_002 | Standing | 1.446309439349e12 | -2.41358 | -9.15796 | -1.22685 | 2.6586354545454545 | -8.621471818181819 | 0.7832245454545453 | 5.8253684164 | 83.86823136159998 | 1.5051609225 | 9.549804223150336 | 9.421158271488634 | 5.0722154545454545 | 0.5364881818181804 | 2.0100745454545454 | 1.1182553719008264 | 0.14821446280991732 | 0.4478087603305785 | 25.727369617329753 | 0.28781956923057694 | 4.040399678284298 | 5.350911544834711 | 6.64813328893311e-2 | 0.7877228821965438 | 2.313203740450614 | 0.25783974264905535 | 0.8875375384717786 |
| user_002 | Standing | 1.446309439358e12 | -2.56686 | -9.15796 | -1.30349 | 2.052478181818182 | -8.66676 | 0.56027 | 6.5887702596 | 83.86823136159998 | 1.6990861801000001 | 9.599796237488585 | 9.416562161867116 | 4.619338181818183 | 0.4911999999999992 | 1.86376 | 1.5011419008264466 | 0.17956785123966928 | 0.6042570247933884 | 21.338285238003312 | 0.2412774399999992 | 3.4736013376000003 | 7.275653235214801 | 8.646951705642343e-2 | 1.1016502629592788 | 2.697341883264856 | 0.29405699627185106 | 1.0495952853168113 |
| user_002 | Standing | 1.446309439365e12 | -2.75846 | -9.08132 | -1.11189 | 1.4567718181818181 | -8.715531818181818 | 0.34428272727272735 | 7.609101571599999 | 82.47037294239999 | 1.2362993721000002 | 9.55592872964737 | 9.427607217433433 | 4.2152318181818185 | 0.36578818181818207 | 1.4561727272727274 | 1.8767440495867773 | 0.21060446280991726 | 0.7347361983471075 | 17.7681792810124 | 0.13380099395785142 | 2.120439011652893 | 8.890306776391661 | 9.85791846531928e-2 | 1.294377728927423 | 2.981661747481035 | 0.31397322282830553 | 1.1377072246089601 |
| user_002 | Standing | 1.446309439406e12 | -2.8351 | -9.11964 | -0.958607 | -1.5496845454545456 | -9.03260090909091 | -0.6694087272727273 | 8.03779201 | 83.1678337296 | 0.918927380449 | 9.598153630779672 | 9.558002976551343 | 1.2854154545454546 | 8.703909090909079e-2 | 0.2891982727272727 | 2.9196666942148766 | 0.2993195041322308 | 1.020040347107438 | 1.6522928907842975 | 7.575803346280971e-3 | 8.3635640948438e-2 | 11.95888436007566 | 0.12769190776318512 | 1.5646831823807708 | 3.458161991589703 | 0.3573400450036143 | 1.2508729681229709 |
| user_002 | Standing | 1.446309439414e12 | -2.8351 | -8.96635 | -1.15021 | -2.1384236363636364 | -9.074404545454547 | -0.8819123636363635 | 8.03779201 | 80.3954323225 | 1.3229830441 | 9.473975267890454 | 9.571195588051067 | 0.6966763636363638 | 0.10805454545454651 | 0.26829763636363646 | 2.975400330578513 | 0.3078758677685945 | 1.0371470743801652 | 0.48535795564958695 | 1.1675784793388658e-2 | 7.19836216783141e-2 | 12.002372353858306 | 0.12873569078467273 | 1.5706435300693327 | 3.464444018000335 | 0.358797562400684 | 1.253253178758918 |
| user_002 | Standing | 1.44630943943e12 | -2.95007 | -8.88971 | -0.996927 | -2.78987 | -9.09879 | -1.0491280909090908 | 8.702913004900001 | 79.02694388409998 | 0.993863443329 | 9.419326957502271 | 9.57699232493614 | 0.16020000000000012 | 0.20908000000000015 | 5.220109090909075e-2 | 2.4864117355371897 | 0.2717823140495863 | 0.8417354876033056 | 2.5664040000000037e-2 | 4.3714446400000065e-2 | 2.724953892099157e-3 | 9.03017986804523 | 9.63240158082641e-2 | 1.1578888306481607 | 3.005025768283066 | 0.31036110550174306 | 1.0760524293212486 |
| user_002 | Standing | 1.446309439438e12 | -2.91174 | -9.00468 | -1.15021 | -2.835157272727273 | -9.084855454545455 | -1.042160818181818 | 8.4782298276 | 81.0842619024 | 1.3229830441 | 9.533387371448828 | 9.57549988387237 | 7.658272727272708e-2 | 8.017545454545427e-2 | 0.1080491818181819 | 2.032263305785124 | 0.23029933884297485 | 0.668824090909091 | 5.8649141165288965e-3 | 6.428103511570204e-3 | 1.1674625691578532e-2 | 6.691861258662209 | 7.074297347017257e-2 | 0.7916410985942772 | 2.586863208339824 | 0.2659755129145775 | 0.8897421528703006 |
| user_002 | Standing | 1.446309439447e12 | -3.02671 | -8.96635 | -0.996927 | -2.8769618181818184 | -9.067436363636363 | -1.0142914545454544 | 9.1609734241 | 80.3954323225 | 0.993863443329 | 9.51579051839252 | 9.567863000318182 | 0.1497481818181816 | 0.10108636363636236 | 1.7364454545454433e-2 | 1.6259369421487604 | 0.19483446280991695 | 0.5009699504132231 | 2.242451795785118e-2 | 1.0218452913222884e-2 | 3.015242816611531e-4 | 4.754055738658079 | 4.973761100773835e-2 | 0.475886570110792 | 2.180379723501867 | 0.22301930635650885 | 0.6898453233231288 |
| user_002 | Standing | 1.446309439479e12 | -2.95007 | -9.00468 | -1.30349 | -2.915229090909091 | -9.04648 | -1.0979539090909094 | 8.702913004900001 | 81.0842619024 | 1.6990861801000001 | 9.564845063428889 | 9.569223294629118 | 3.48409090909092e-2 | 4.180000000000028e-2 | 0.20553609090909064 | 0.43384776859504137 | 0.10800570247933873 | 0.19035499999999994 | 1.2138889462809993e-3 | 1.7472400000000236e-3 | 4.224508466618997e-2 | 0.5615326180894062 | 1.6430564487828706e-2 | 4.903380208257624e-2 | 0.749354801205281 | 0.12818176347604485 | 0.2214357741706977 |
| user_002 | Standing | 1.446309439511e12 | -2.91174 | -9.00468 | -0.920286 | -2.95355 | -9.015127272727273 | -1.0979539090909092 | 8.4782298276 | 81.0842619024 | 0.8469263217960001 | 9.508386721825948 | 9.55133171916616 | 4.18099999999999e-2 | 1.044727272727286e-2 | 0.17766790909090913 | 8.296809917355369e-2 | 6.778289256198326e-2 | 0.14284077685950408 | 1.748076099999992e-3 | 1.0914550743801931e-4 | 3.156588592073555e-2 | 8.785557995642373e-3 | 8.222870272652123e-3 | 2.737519115496168e-2 | 9.373130744656437e-2 | 9.068004340896692e-2 | 0.16545449874500748 |
| user_002 | Standing | 1.446309439593e12 | -2.6435 | -8.92803 | -1.03525 | -2.7480109090909095 | -9.001192727272725 | -0.9934444545454544 | 6.98809225 | 79.7097196809 | 1.0717425625 | 9.368540681098631 | 9.46560887552769 | 0.10451090909090954 | 7.316272727272555e-2 | 4.180554545454562e-2 | 0.1415662809917357 | 3.610330578512395e-2 | 0.15454909917355375 | 1.0922530119008358e-2 | 5.352784661983218e-3 | 1.74770363075208e-3 | 2.8163932671224665e-2 | 2.3986276106686684e-3 | 3.2164896790317817e-2 | 0.167821132969673 | 4.897578596274559e-2 | 0.1793457465074592 |
| user_002 | Standing | 1.446309439616e12 | -2.75846 | -9.04299 | -0.920286 | -2.6853045454545454 | -8.997706363636365 | -1.017830090909091 | 7.609101571599999 | 81.7756681401 | 0.8469263217960001 | 9.49903658449087 | 9.446218153980428 | 7.315545454545447e-2 | 4.528363636363508e-2 | 9.754409090909089e-2 | 0.14884958677685975 | 3.958702479338815e-2 | 0.11242859504132229 | 5.351720529752055e-3 | 2.0506077223139334e-3 | 9.514849671280988e-3 | 2.8778300438317098e-2 | 2.593859808640096e-3 | 2.0548568995271223e-2 | 0.16964168249082268 | 5.092995001607695e-2 | 0.14334772057926565 |
| user_002 | Standing | 1.446309439624e12 | -2.6435 | -9.19628 | -0.805325 | -2.6574345454545454 | -9.015124545454547 | -1.000411727272727 | 6.98809225 | 84.57156583839999 | 0.6485483556249999 | 9.602510424051879 | 9.453000156778382 | 1.3934545454545422e-2 | 0.18115545454545234 | 0.19508672727272713 | 0.1491659504132233 | 5.3205619834710276e-2 | 0.12446316528925618 | 1.9417155702479248e-4 | 3.281729871156945e-2 | 3.805883115798342e-2 | 2.8786016259804703e-2 | 5.487897918707642e-3 | 2.3650999151300523e-2 | 0.16966442249276867 | 7.408034772264263e-2 | 0.1537888134790711 |
| user_002 | Standing | 1.446309439641e12 | -2.87342 | -8.92803 | -0.958607 | -2.6818199999999996 | -9.001189090909092 | -1.0004115454545455 | 8.2565424964 | 79.7097196809 | 0.918927380449 | 9.427894227119278 | 9.446675907091665 | 0.19160000000000021 | 7.315909090909223e-2 | 4.180454545454548e-2 | 0.13808049586776872 | 6.0806280991735046e-2 | 0.11622894214876026 | 3.671056000000008e-2 | 5.352252582644821e-3 | 1.7476200206611595e-3 | 2.421915496558982e-2 | 6.044008924868438e-3 | 1.9127603799277224e-2 | 0.15562504607417735 | 7.774322430198298e-2 | 0.13830258059514733 |
| user_002 | Standing | 1.446309439682e12 | -2.87342 | -9.15796 | -1.18853 | -2.744525454545455 | -9.077831818181819 | -0.9272538181818182 | 8.2565424964 | 83.86823136159998 | 1.4126035609000003 | 9.671472350107814 | 9.530021680868458 | 0.12889454545454493 | 8.012818181818027e-2 | 0.2612761818181819 | 9.595851239669423e-2 | 8.519438016528837e-2 | 0.10482755371900822 | 1.6613803847933747e-2 | 6.420525521487355e-3 | 6.826524318548766e-2 | 1.2397225940646135e-2 | 1.0105455614575334e-2 | 1.8697265879660405e-2 | 0.11134283066567931 | 0.1005258952438392 | 0.13673794601229172 |
| user_002 | Standing | 1.446309439779e12 | -3.14167 | -8.92803 | -1.03525 | -3.1904399999999997 | -8.837456363636363 | -1.1362751818181818 | 9.8700903889 | 79.7097196809 | 1.0717425625 | 9.521110892763511 | 9.466807733922074 | 4.876999999999976e-2 | 9.057363636363647e-2 | 0.10102518181818176 | 0.20078768595041332 | 0.18590322314049648 | 0.10704351239669427 | 2.378512899999976e-3 | 8.20358360413225e-3 | 1.0206087361396683e-2 | 4.9067638348234466e-2 | 4.669208395063887e-2 | 1.500442246723141e-2 | 0.22151216298035298 | 0.21608351151959482 | 0.12249254045545553 |
| user_002 | Standing | 1.446309440013e12 | -2.91174 | -9.11964 | -1.18853 | -3.002320909090909 | -9.060413636363636 | -0.8924176363636362 | 8.4782298276 | 83.1678337296 | 1.4126035609000003 | 9.646692029815195 | 9.588635056175285 | 9.058090909090888e-2 | 5.9226363636364354e-2 | 0.29611236363636384 | 7.44266115702479e-2 | 0.1254145454545451 | 0.15423192561983476 | 8.204901091735498e-3 | 3.5077621495868618e-3 | 8.768253189831417e-2 | 6.577076111945887e-3 | 2.27942267930878e-2 | 3.7267546187075146e-2 | 8.10991745454039e-2 | 0.1509775704967059 | 0.19304804113762758 |
| user_002 | Standing | 1.446309440094e12 | -3.17999 | -9.00468 | -0.728685 | -3.065028181818182 | -9.018611818181817 | -1.073569 | 10.1123364001 | 81.0842619024 | 0.530981829225 | 9.577451651234007 | 9.587080246425797 | 0.11496181818181794 | 1.393181818181688e-2 | 0.34488399999999997 | 8.012586776859494e-2 | 6.777396694214909e-2 | 0.1513813140495867 | 1.3216219639669367e-2 | 1.9409555785120339e-4 | 0.11894497345599998 | 7.150541952441762e-3 | 7.041439655522178e-3 | 3.746930176322839e-2 | 8.45608771976838e-2 | 8.391328652556863e-2 | 0.1935698885757503 |
| user_002 | Standing | 1.446309440167e12 | -3.33327 | -8.85139 | -0.613724 | -3.1799899999999997 | -8.955903636363637 | -0.8436462727272727 | 11.1106888929 | 78.34710493210001 | 0.37665714817600005 | 9.478103764634358 | 9.542767424351403 | 0.15328000000000053 | 0.10451363636363631 | 0.22992227272727261 | 8.804140495867778e-2 | 5.5741404958677035e-2 | 0.15961630578512392 | 2.3494758400000162e-2 | 1.0923100185950402e-2 | 5.286425149607433e-2 | 9.90503651697974e-3 | 6.0904046268218555e-3 | 3.276510562810293e-2 | 9.952404994261306e-2 | 7.804104450109478e-2 | 0.18101134115878742 |
| user_002 | Standing | 1.446309440208e12 | -3.21831 | -8.92803 | -0.690364 | -3.246179090909091 | -8.941968181818181 | -0.6694626363636363 | 10.357519256099998 | 79.7097196809 | 0.476602452496 | 9.515452768496935 | 9.539032890870038 | 2.7869090909091288e-2 | 1.3938181818181405e-2 | 2.0901363636363635e-2 | 0.10007537190082677 | 7.189330578512373e-2 | 0.1862184545454545 | 7.766862280991947e-4 | 1.942729123966827e-4 | 4.3686700185950405e-4 | 1.291020204718265e-2 | 7.161748909316253e-3 | 4.620502815389105e-2 | 0.11362307004821974 | 8.462711686756351e-2 | 0.21495354882832488 |
| user_002 | Standing | 1.446309440255e12 | -3.14167 | -8.88971 | -0.767005 | -3.21831 | -8.924548181818182 | -0.641592909090909 | 9.8700903889 | 79.02694388409998 | 0.5882966700250001 | 9.45966864869087 | 9.510356482993483 | 7.663999999999982e-2 | 3.4838181818182434e-2 | 0.125412090909091 | 9.785851239669441e-2 | 7.189198347107445e-2 | 0.15993210743801647 | 5.873689599999972e-3 | 1.2136989123967372e-3 | 1.5728192546190106e-2 | 1.1483703051540239e-2 | 7.808080333508638e-3 | 3.854711547508264e-2 | 0.10716204109450435 | 8.836334270221242e-2 | 0.19633419334156402 |
| user_002 | Standing | 1.446309440264e12 | -3.06503 | -9.11964 | -0.805325 | -3.1904409090909094 | -8.948934545454545 | -0.6833968181818182 | 9.3944089009 | 83.1678337296 | 0.6485483556249999 | 9.654573578678916 | 9.526400963474687 | 0.12541090909090924 | 0.17070545454545538 | 0.12192818181818177 | 9.374148760330593e-2 | 7.790942148760345e-2 | 0.13111266942148758 | 1.57278961190083e-2 | 2.9140352211570533e-2 | 1.4866481521487592e-2 | 1.0264614582719784e-2 | 9.464194154019558e-3 | 2.238315203485424e-2 | 0.1013144342269145 | 9.728408993262751e-2 | 0.1496099997822814 |
| user_002 | Standing | 1.446309440272e12 | -3.14167 | -8.96635 | -0.690364 | -3.169539090909091 | -8.945450000000001 | -0.6868804545454544 | 9.8700903889 | 80.3954323225 | 0.476602452496 | 9.52586611095789 | 9.516273010080207 | 2.7869090909090843e-2 | 2.0899999999999253e-2 | 3.4835454545455447e-3 | 8.234049586776869e-2 | 7.537520661157031e-2 | 0.12002820661157027 | 7.766862280991699e-4 | 4.3680999999996876e-4 | 1.2135088933884925e-5 | 8.199335294365147e-3 | 9.287619276784394e-3 | 2.095440710115026e-2 | 9.05501810841102e-2 | 9.637229517233879e-2 | 0.14475637153904578 |
| user_002 | Standing | 1.446309440369e12 | -3.21831 | -8.92803 | -0.652044 | -3.1939245454545455 | -8.924548181818182 | -0.6172073636363635 | 10.357519256099998 | 79.7097196809 | 0.42516137793599995 | 9.51274935625532 | 9.499286230174913 | 2.4385454545454266e-2 | 3.4818181818181415e-3 | 3.483663636363643e-2 | 5.953851239669412e-2 | 7.917504132231427e-2 | 8.899176033057847e-2 | 5.946503933884161e-4 | 1.2123057851239389e-5 | 1.2135912331322361e-3 | 5.207328120210363e-3 | 9.433200728324624e-3 | 9.89077617961757e-3 | 7.216181899183503e-2 | 9.712466591100648e-2 | 9.945238146780383e-2 |
| user_002 | Standing | 1.446309440385e12 | -3.06503 | -9.04299 | -0.690364 | -3.1556045454545454 | -8.948933636363638 | -0.6520440909090909 | 9.3944089009 | 81.7756681401 | 0.476602452496 | 9.57322722458294 | 9.511886865986183 | 9.057454545454524e-2 | 9.405636363636205e-2 | 3.83199090909091e-2 | 5.922181818181806e-2 | 7.157404958677706e-2 | 8.455795867768595e-2 | 8.203748284297482e-3 | 8.846599540495569e-3 | 1.4684154327355378e-3 | 4.553102362734779e-3 | 8.400471258151765e-3 | 9.930491764654397e-3 | 6.74766801401401e-2 | 9.165408478705009e-2 | 9.965185279087588e-2 |
| user_002 | Standing | 1.446309440402e12 | -3.02671 | -8.92803 | -0.652044 | -3.145153636363636 | -8.966352727272728 | -0.6485603636363636 | 9.1609734241 | 79.7097196809 | 0.42516137793599995 | 9.449648378798864 | 9.524740607271267 | 0.11844363636363608 | 3.832272727272823e-2 | 3.4836363636363554e-3 | 6.903933884297513e-2 | 6.365760330578528e-2 | 7.632391735537192e-2 | 1.4028894995041256e-2 | 1.468631425619908e-3 | 1.213572231404953e-5 | 5.942090943951906e-3 | 6.592294127047354e-3 | 9.266348353708495e-3 | 7.708495925893653e-2 | 8.119294382547879e-2 | 9.626187383231481e-2 |
| user_002 | Standing | 1.446309440443e12 | -3.21831 | -8.85139 | -0.537083 | -3.1939245454545455 | -8.952417272727272 | -0.6799133636363636 | 10.357519256099998 | 78.34710493210001 | 0.28845814888899995 | 9.433614489530989 | 9.530466504098127 | 2.4385454545454266e-2 | 0.10102727272727208 | 0.1428303636363636 | 8.044033057851228e-2 | 6.270776859504178e-2 | 8.455816528925623e-2 | 5.946503933884161e-4 | 1.0206509834710612e-2 | 2.0400512776495853e-2 | 8.570026448685191e-3 | 6.867015413223179e-3 | 1.1296374451510148e-2 | 9.257443733928493e-2 | 8.286745690090398e-2 | 0.1062844036136542 |
| user_002 | Standing | 1.446309440523e12 | -3.29495 | -8.92803 | -0.652044 | -3.1799899999999997 | -8.966352727272726 | -0.7426192727272727 | 10.856695502500001 | 79.7097196809 | 0.42516137793599995 | 9.538950495800677 | 9.543716163405005 | 0.1149600000000004 | 3.832272727272645e-2 | 9.057527272727273e-2 | 7.695669421487607e-2 | 7.47438016528927e-2 | 9.50090330578512e-2 | 1.321580160000009e-2 | 1.4686314256197718e-3 | 8.203880029619835e-3 | 7.959930590533444e-3 | 9.852753776408735e-3 | 1.2180060023302025e-2 | 8.921844310754051e-2 | 9.926103856200949e-2 | 0.11036330922594711 |
| user_002 | Standing | 1.446309440604e12 | -2.87342 | -8.85139 | -0.652044 | -3.1730218181818186 | -8.938482727272726 | -0.5963053636363637 | 8.2565424964 | 78.34710493210001 | 0.42516137793599995 | 9.32892323939028 | 9.504807224694117 | 0.29960181818181875 | 8.709272727272577e-2 | 5.5738636363636296e-2 | 0.1399796694214878 | 6.840826446280976e-2 | 8.455803305785121e-2 | 8.976124945785158e-2 | 7.585143143801391e-3 | 3.1067955836776785e-3 | 2.417485405169051e-2 | 5.938182953268183e-3 | 1.1084500493594286e-2 | 0.155482648715831 | 7.70596064956744e-2 | 0.10528295443040286 |
| user_002 | Standing | 1.446309440636e12 | -3.14167 | -9.00468 | -0.422122 | -3.07548 | -8.95590272727273 | -0.5754033636363636 | 9.8700903889 | 81.0842619024 | 0.178186982884 | 9.546336432065655 | 9.487635980915412 | 6.618999999999975e-2 | 4.877727272727128e-2 | 0.15328136363636358 | 0.12509504132231408 | 7.125950413223091e-2 | 7.695739669421485e-2 | 4.381116099999967e-3 | 2.379222334710603e-3 | 2.349517643822312e-2 | 2.2137127736138257e-2 | 6.930217224943577e-3 | 9.248688850788124e-3 | 0.14878550916046313 | 8.324792625010893e-2 | 9.617010372661622e-2 |
| user_002 | Standing | 1.446309440709e12 | -3.29495 | -8.96635 | -0.537083 | -3.1486372727272722 | -8.95590181818182 | -0.4325730909090909 | 10.856695502500001 | 80.3954323225 | 0.28845814888899995 | 9.567684462496086 | 9.504070361761608 | 0.14631272727272782 | 1.0448181818180302e-2 | 0.10450990909090907 | 8.234082644628084e-2 | 8.392644628099158e-2 | 0.10989387603305784 | 2.140741416198363e-2 | 1.0916450330575344e-4 | 1.0922321098190078e-2 | 7.972127924868498e-3 | 8.659925591434994e-3 | 1.8666175948377908e-2 | 8.92867735158377e-2 | 9.305872120029908e-2 | 0.13662421435594024 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 | -3.1521209090909093 | -8.952418181818182 | -0.41863845454545456 | 9.3944089009 | 79.02694388409998 | 0.147303975204 | 9.411092219301858 | 9.50122307587 | 8.709090909090911e-2 | 6.27081818181825e-2 | 3.483645454545459e-2 | 8.42410743801651e-2 | 8.804347107437994e-2 | 0.10894376033057851 | 7.584826446280995e-3 | 3.932316066942234e-3 | 1.2135785652975235e-3 | 8.263396200676159e-3 | 8.9898190453794e-3 | 1.8590048705084142e-2 | 9.090322436897472e-2 | 9.48146562793928e-2 | 0.1363453288715244 |
| user_002 | Standing | 1.446309440757e12 | -3.06503 | -9.00468 | -0.460442 | -3.124250909090909 | -9.001190909090907 | -0.47437718181818184 | 9.3944089009 | 81.0842619024 | 0.21200683536400003 | 9.523165316146937 | 9.540537199617482 | 5.9220909090909046e-2 | 3.4890909090936617e-3 | 1.3935181818181819e-2 | 8.74085123966943e-2 | 8.044330578512415e-2 | 5.573867768595043e-2 | 3.5071160735537137e-3 | 1.2173755371920035e-5 | 1.9418929230578515e-4 | 1.0291470260030057e-2 | 8.978836671525157e-3 | 4.037954539944403e-3 | 0.10144688393455 | 9.475672362173122e-2 | 6.354490176201709e-2 |
| user_002 | Standing | 1.446309440968e12 | -3.37159 | -8.85139 | -0.460442 | -3.305400909090909 | -8.93499818181818 | -0.453475090909091 | 11.3676191281 | 78.34710493210001 | 0.21200683536400003 | 9.482970573378577 | 9.538478337551858 | 6.618909090909098e-2 | 8.360818181817997e-2 | 6.966909090909024e-3 | 7.410644628099185e-2 | 8.456008264462754e-2 | 7.315664462809915e-2 | 4.3809957553719095e-3 | 6.99032806694184e-3 | 4.85378222809908e-5 | 8.214780759128489e-3 | 9.829378283621232e-3 | 8.988266418359123e-3 | 9.063542772629525e-2 | 9.914322106740951e-2 | 9.48064682305966e-2 |
| user_002 | Standing | 1.446309440976e12 | -3.17999 | -8.81307 | -0.728685 | -3.305400909090909 | -8.921063636363634 | -0.48831172727272737 | 10.1123364001 | 77.6702028249 | 0.530981829225 | 9.397527390448245 | 9.527360093705266 | 0.1254109090909088 | 0.10799363636363424 | 0.24037327272727266 | 8.329057851239675e-2 | 8.835999999999929e-2 | 8.392442148760329e-2 | 1.572789611900819e-2 | 1.1662625495040864e-2 | 5.77793102416198e-2 | 9.590530370548467e-3 | 1.0491278978812782e-2 | 1.2889442742334329e-2 | 9.793125328794923e-2 | 0.10242694459375805 | 0.11353168166786894 |
| user_002 | Standing | 1.446309441033e12 | -2.91174 | -9.15796 | -0.422122 | -3.071993636363636 | -8.941968181818181 | -0.589338 | 8.4782298276 | 83.86823136159998 | 0.178186982884 | 9.618973342934472 | 9.475820568928262 | 0.160253636363636 | 0.21599181818181812 | 0.16721600000000003 | 0.17386785123966939 | 0.10672925619834589 | 0.11021046280991734 | 2.5681227967768474e-2 | 4.665246552148757e-2 | 2.796119065600001e-2 | 3.920310875003756e-2 | 1.6438388672426554e-2 | 1.7255076525382412e-2 | 0.19799774935599032 | 0.12821227972556512 | 0.13135857994582012 |
| user_002 | Standing | 1.446309441106e12 | -2.98839 | -8.96635 | -0.498763 | -2.9674845454545458 | -9.053445454545455 | -0.4778608181818182 | 8.9304747921 | 80.3954323225 | 0.24876453016900002 | 9.464389660446626 | 9.539829440026057 | 2.0905454545454116e-2 | 8.70954545454552e-2 | 2.090218181818182e-2 | 9.279487603305793e-2 | 7.347512396694161e-2 | 9.279223140495872e-2 | 4.3703802975204815e-4 | 7.585618202479452e-3 | 4.3690120476033066e-4 | 1.3351447519158555e-2 | 1.0077673469045744e-2 | 1.2359885965731031e-2 | 0.11554846394114703 | 0.10038761611396968 | 0.11117502401947585 |
| user_002 | Standing | 1.446309441162e12 | -3.06503 | -8.85139 | -0.537083 | -3.037160909090909 | -9.053446363636365 | -0.5335994545454544 | 9.3944089009 | 78.34710493210001 | 0.28845814888899995 | 9.38242889564792 | 9.564728399777655 | 2.7869090909091288e-2 | 0.20205636363636437 | 3.4835454545455447e-3 | 5.098809917355374e-2 | 6.302636363636341e-2 | 5.447196694214875e-2 | 7.766862280991947e-4 | 4.082677408595071e-2 | 1.2135088933884925e-5 | 5.113660344703236e-3 | 6.733654338918086e-3 | 4.6513847852577e-3 | 7.1509861870257e-2 | 8.205884680470525e-2 | 6.820106146723598e-2 |
| user_002 | Standing | 1.446309441389e12 | -3.33327 | -8.92803 | -0.345482 | -3.347204545454545 | -8.900162727272727 | -0.3594163636363636 | 11.1106888929 | 79.7097196809 | 0.119357812324 | 9.536234392364944 | 9.51624032343799 | 1.3934545454544978e-2 | 2.7867272727272407e-2 | 1.3934363636363578e-2 | 8.455735537190089e-2 | 9.374289256198312e-2 | 8.740813223140496e-2 | 1.941715570247801e-4 | 7.765848892561805e-4 | 1.941664899504116e-4 | 8.604227120661164e-3 | 1.3292391518181741e-2 | 1.1315045705301273e-2 | 9.275897326221956e-2 | 0.11529263427548934 | 0.1063722036309358 |
| user_002 | Standing | 1.446309441478e12 | -3.37159 | -8.88971 | -0.383802 | -3.343720909090909 | -8.889711818181818 | -0.32806318181818184 | 11.3676191281 | 79.02694388409998 | 0.147303975204 | 9.515349020787623 | 9.504447769985175 | 2.7869090909090843e-2 | 1.81818181843596e-6 | 5.573881818181814e-2 | 7.473983471074379e-2 | 6.999190082644527e-2 | 8.202456198347109e-2 | 7.766862280991699e-4 | 3.305785124891094e-12 | 3.1068158523057804e-3 | 7.850709089706979e-3 | 7.146110540195194e-3 | 8.271189009026299e-3 | 8.860422726770421e-2 | 8.453467063989303e-2 | 9.094607748015468e-2 |
| user_002 | Standing | 1.446309441607e12 | -3.25663 | -8.81307 | -0.383802 | -3.253146363636364 | -8.858357272727273 | -0.4465077272727273 | 10.6056389569 | 77.6702028249 | 0.147303975204 | 9.403358216988439 | 9.448580993474923 | 3.4836363636361334e-3 | 4.528727272727373e-2 | 6.270572727272733e-2 | 8.709090909090915e-2 | 6.745603305785056e-2 | 7.157333057851244e-2 | 1.2135722314047982e-5 | 2.050937071074471e-3 | 3.93200823280166e-3 | 1.3511471925469594e-2 | 6.41544745364378e-3 | 1.040376288128701e-2 | 0.11623885720992612 | 8.009648839770556e-2 | 0.10199883764674483 |
| user_002 | Standing | 1.446309441931e12 | -3.21831 | -9.08132 | -0.1922 | -3.1974081818181816 | -8.931516363636364 | -0.27580818181818184 | 10.357519256099998 | 82.47037294239999 | 3.694084e-2 | 9.63664013225045 | 9.492035489223916 | 2.0901818181818133e-2 | 0.14980363636363592 | 8.360818181818183e-2 | 7.60066115702478e-2 | 0.12478074380165212 | 0.1165442561983471 | 4.3688600330578305e-4 | 2.244112946776846e-2 | 6.990328066942151e-3 | 8.611949853042809e-3 | 2.0636293135762387e-2 | 1.9541015617250947e-2 | 9.280059187873108e-2 | 0.14365337843490625 | 0.13978918276193958 |
| user_002 | Standing | 1.446309441939e12 | -3.06503 | -9.00468 | -0.268841 | -3.1834736363636362 | -8.93500090909091 | -0.2618735454545455 | 9.3944089009 | 81.0842619024 | 7.2275483281e-2 | 9.515826095856365 | 9.490221315119124 | 0.11844363636363608 | 6.967909090909075e-2 | 6.967454545454499e-3 | 8.202380165289243e-2 | 0.12636454545454467 | 0.11686097520661157 | 1.4028894995041256e-2 | 4.855175709917333e-3 | 4.854542284297456e-5 | 9.639073259804641e-3 | 2.0829416150037368e-2 | 1.9544325647606317e-2 | 9.817878212630589e-2 | 0.14432399713851252 | 0.13980102162576036 |
| user_002 | Standing | 1.446309442045e12 | -2.95007 | -9.19628 | -0.15388 | -3.1625718181818185 | -9.039512727272728 | -0.13646172727272726 | 8.702913004900001 | 84.57156583839999 | 2.3679054399999996e-2 | 9.659097157483197 | 9.579298743119958 | 0.21250181818181835 | 0.1567672727272722 | 1.7418272727272727e-2 | 0.10640925619834686 | 9.691314049586824e-2 | 8.677491735537189e-2 | 4.515702273057858e-2 | 2.4575977798346943e-2 | 3.0339622480165287e-4 | 1.5083599588880496e-2 | 1.2350613757099936e-2 | 1.0845087555632604e-2 | 0.12281530681832983 | 0.11113331524389947 | 0.10413975012276822 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 | -2.998838181818182 | -9.08480090909091 | -0.10162500000000002 | 8.2565424964 | 82.47037294239999 | 7.2275483281e-2 | 9.528860945678712 | 9.568464948931542 | 0.1254181818181821 | 3.4809090909107e-3 | 0.16721599999999998 | 0.12541347107438028 | 8.899380165289292e-2 | 7.854080991735538e-2 | 1.572972033057858e-2 | 1.2116728099184755e-5 | 2.796119065599999e-2 | 2.0858878497145062e-2 | 1.349019009534193e-2 | 1.0030882045673928e-2 | 0.14442603123102518 | 0.11614727760624409 | 0.10015429119949842 |
| user_002 | Standing | 1.446309442611e12 | -2.60518 | -9.11964 | -5.99e-4 | -2.8490390909090912 | -9.077834545454547 | -8.07229090909091e-2 | 6.7869628323999995 | 83.1678337296 | 3.5880100000000004e-7 | 9.484450269825922 | 9.517987482543768 | 0.2438590909090914 | 4.180545454545381e-2 | 8.012390909090909e-2 | 0.17006842975206615 | 6.872446280991747e-2 | 6.777323140495868e-2 | 5.946725621900851e-2 | 1.7476960297520049e-3 | 6.419840808008265e-3 | 4.2711685068820476e-2 | 7.717777571600379e-3 | 8.636367726251688e-3 | 0.206668055269363 | 8.785088258862503e-2 | 9.29320597331819e-2 |
| user_002 | Standing | 1.44630944274e12 | -2.41358 | -9.11964 | -5.99e-4 | -2.737560909090909 | -9.10222 | -7.375563636363637e-2 | 5.8253684164 | 83.1678337296 | 3.5880100000000004e-7 | 9.433620858652366 | 9.509199078564167 | 0.32398090909090893 | 1.7419999999999547e-2 | 7.315663636363637e-2 | 0.22264024793388434 | 7.537578512396675e-2 | 7.569057024793388e-2 | 0.1049636294553718 | 3.034563999999842e-4 | 5.351893444041323e-3 | 6.229692536341098e-2 | 8.963426320135297e-3 | 9.325901606570248e-3 | 0.24959352027528875 | 9.4675373356197e-2 | 9.657070780816639e-2 |
| user_002 | Standing | 1.446309444812e12 | -2.33694 | -9.2346 | 0.229323 | -2.2045618181818183 | -9.213698181818183 | 0.12481318181818181 | 5.461288563599999 | 85.27783716 | 5.2589038329e-2 | 9.528468647265887 | 9.475980846384282 | 0.1323781818181815 | 2.090181818181769e-2 | 0.10450981818181819 | 0.10704264462809908 | 4.3387107438016284e-2 | 7.157342975206611e-2 | 1.752398302148752e-2 | 4.368860033057645e-4 | 1.0922302096396696e-2 | 1.5613158380766323e-2 | 4.877457122764723e-3 | 7.206518220723518e-3 | 0.12495262454533047 | 6.983879382381059e-2 | 8.489121403728138e-2 |
| user_002 | Standing | 1.446309446561e12 | -1.8771 | -9.2346 | 0.229323 | -1.8561981818181819 | -9.27292 | 0.18403527272727274 | 3.52350441 | 85.27783716 | 5.2589038329e-2 | 9.426236290711634 | 9.45880943756542 | 2.0901818181818133e-2 | 3.83199999999988e-2 | 4.528772727272726e-2 | 4.243727272727282e-2 | 2.5968925619834528e-2 | 2.5336e-2 | 4.3688600330578305e-4 | 1.468422399999908e-3 | 2.050978241528924e-3 | 2.131493715326829e-3 | 9.642383002253733e-4 | 1.193745043338843e-3 | 4.6168102791070253e-2 | 3.1052186722119478e-2 | 3.4550615672355865e-2 |
| user_002 | Standing | 1.44630944695e12 | -1.72382 | -9.31124 | 7.6042e-2 | -1.8527145454545455 | -9.279887272727272 | 0.177068 | 2.9715553923999996 | 86.6991903376 | 5.782385764e-3 | 9.469769169085591 | 9.465045650120201 | 0.1288945454545456 | 3.135272727272742e-2 | 0.101026 | 4.845429752066124e-2 | 2.0901818181818174e-2 | 4.750462809917355e-2 | 1.661380384793392e-2 | 9.829935074380258e-4 | 1.0206252676000001e-2 | 3.42117965807664e-3 | 7.656537532682156e-4 | 3.369363647926372e-3 | 5.849085106302215e-2 | 2.7670449097696545e-2 | 5.804621992797095e-2 |
| user_002 | Standing | 1.446309447921e12 | -1.60885 | -9.27292 | 0.114362 | -1.755169090909091 | -9.30078909090909 | 0.15268227272727272 | 2.5883983225 | 85.98704532639998 | 1.3078667044000002e-2 | 9.412147593187433 | 9.466772391953667 | 0.146319090909091 | 2.7869090909090843e-2 | 3.832027272727272e-2 | 7.632619834710748e-2 | 2.9452561983471914e-2 | 4.085393388429751e-2 | 2.140927636446284e-2 | 7.766862280991699e-4 | 1.4684433018925611e-3 | 7.252263568970701e-3 | 1.1771650644628554e-3 | 2.70630033675432e-3 | 8.516022292696691e-2 | 3.430983917862127e-2 | 5.202211392046963e-2 |
| user_002 | Standing | 1.446309448245e12 | -1.64717 | -9.31124 | 0.229323 | -1.7168481818181818 | -9.304272727272725 | 0.18055163636363636 | 2.7131690089 | 86.6991903376 | 5.2589038329e-2 | 9.458591247370245 | 9.46344627332974 | 6.967818181818175e-2 | 6.967272727274931e-3 | 4.877136363636364e-2 | 6.080867768595046e-2 | 3.135272727272775e-2 | 3.927053719008264e-2 | 4.855049021487594e-3 | 4.854288925622906e-5 | 2.3786459109504136e-3 | 5.8103029906085716e-3 | 1.185991044327584e-3 | 2.272729891622089e-3 | 7.622534349288675e-2 | 3.4438220690500024e-2 | 4.767315692947226e-2 |
| user_002 | Standing | 1.446309448309e12 | -1.64717 | -9.27292 | 0.152682 | -1.7133645454545452 | -9.297305454545453 | 0.18055163636363636 | 2.7131690089 | 85.98704532639998 | 2.3311793124000002e-2 | 9.419316648697185 | 9.45597525917587 | 6.619454545454517e-2 | 2.4385454545454266e-2 | 2.7869636363636346e-2 | 5.859165289256197e-2 | 2.8819173553719324e-2 | 4.1804115702479334e-2 | 4.381717847933847e-3 | 5.946503933884161e-4 | 7.767166310413214e-4 | 5.462725905409464e-3 | 9.918194873027757e-4 | 2.343340494437265e-3 | 7.39102557525643e-2 | 3.14931657237372e-2 | 4.840806228757009e-2 |
| user_002 | Standing | 1.446309449022e12 | -1.60885 | -9.34956 | 0.152682 | -1.675042727272727 | -9.304272727272725 | 0.19100263636363637 | 2.5883983225 | 87.41427219360001 | 2.3311793124000002e-2 | 9.4882022696201 | 9.456044735618114 | 6.619272727272718e-2 | 4.528727272727551e-2 | 3.832063636363636e-2 | 3.3890826446281046e-2 | 3.040264462810007e-2 | 4.465419834710744e-2 | 4.381477143801641e-3 | 2.050937071074632e-3 | 1.4684711713140496e-3 | 1.5260826492111191e-3 | 1.2444631609316841e-3 | 2.862969195238167e-3 | 3.906510782285285e-2 | 3.5276949427801775e-2 | 5.3506721028653656e-2 |
| user_002 | Standing | 1.446309449799e12 | -1.76214 | -9.31124 | 0.114362 | -1.6994281818181818 | -9.297305454545453 | 0.15964972727272728 | 3.1051373796000004 | 86.6991903376 | 1.3078667044000002e-2 | 9.477204565917315 | 9.453145818693523 | 6.271181818181826e-2 | 1.393454545454631e-2 | 4.528772727272727e-2 | 3.64233057851239e-2 | 2.248528925619908e-2 | 6.333940495867768e-2 | 3.932772139669431e-3 | 1.9417155702481723e-4 | 2.0509782415289255e-3 | 2.163873705935382e-3 | 8.417778296018074e-4 | 5.90467196818332e-3 | 4.651745592716117e-2 | 2.9013407755756775e-2 | 7.684186338307603e-2 |
| user_002 | Standing | 1.446309454073e12 | -1.68549 | -9.27292 | 0.114362 | -1.7029127272727274 | -9.297305454545453 | 0.20493736363636364 | 2.8408765400999996 | 85.98704532639998 | 1.3078667044000002e-2 | 9.425550410111018 | 9.454579743223052 | 1.7422727272727423e-2 | 2.4385454545454266e-2 | 9.057536363636363e-2 | 6.144264462809928e-2 | 3.293619834710784e-2 | 2.94527438016529e-2 | 3.0355142561983993e-4 | 5.946503933884161e-4 | 8.203896497859504e-3 | 5.386659989331347e-3 | 1.8711077313298436e-3 | 1.596423004198347e-3 | 7.339386888106762e-2 | 4.3256302793117256e-2 | 3.995526253446906e-2 |
| user_002 | Standing | 1.446309454591e12 | -1.72382 | -9.31124 | 0.152682 | -1.7029136363636364 | -9.283370909090909 | 0.19796990909090909 | 2.9715553923999996 | 86.6991903376 | 2.3311793124000002e-2 | 9.470694669512053 | 9.440886438934198 | 2.0906363636363556e-2 | 2.7869090909090843e-2 | 4.528790909090907e-2 | 6.777768595041332e-2 | 3.958677685950414e-2 | 5.383851239669421e-2 | 4.370760404958644e-4 | 7.766862280991699e-4 | 2.0509947098264446e-3 | 6.0818297021788285e-3 | 2.042111091209604e-3 | 4.07988566014425e-3 | 7.798608659356378e-2 | 4.5189723292022976e-2 | 6.387398265447561e-2 |
| user_002 | Standing | 1.446309455109e12 | -1.80046 | -9.2346 | 0.152682 | -1.7307845454545456 | -9.279887272727272 | 0.19100245454545456 | 3.2416562115999996 | 85.27783716 | 2.3311793124000002e-2 | 9.409718654918647 | 9.44219314863774 | 6.967545454545432e-2 | 4.5287272727271954e-2 | 3.8320454545454546e-2 | 4.9409008264462866e-2 | 2.7552396694214953e-2 | 5.542198347107437e-2 | 4.8546689661156705e-3 | 2.05093707107431e-3 | 1.468457236570248e-3 | 3.2373997262960265e-3 | 9.653415477084801e-4 | 3.4808043690803896e-3 | 5.689815222215944e-2 | 3.1069946052551815e-2 | 5.8998342087556915e-2 |
| user_002 | Standing | 1.446309455692e12 | -1.68549 | -9.31124 | 0.191003 | -1.7307836363636364 | -9.29033818181818 | 0.19448636363636365 | 2.8408765400999996 | 86.6991903376 | 3.6482146009e-2 | 9.464488841121268 | 9.452370650821058 | 4.529363636363648e-2 | 2.0901818181819465e-2 | 3.4833636363636455e-3 | 3.420619834710752e-2 | 2.850247933884327e-2 | 3.483647107438016e-2 | 2.051513495041333e-3 | 4.368860033058388e-4 | 1.213382222314056e-5 | 1.5448672236664225e-3 | 1.5246880216378755e-3 | 2.060896010293764e-3 | 3.930479899028136e-2 | 3.904725370160974e-2 | 4.5397092531281824e-2 |
| user_002 | Walking | 1.446034820327e12 | -5.40257 | -10.92069 | 1.34061 | -4.641632857142858 | -9.152482857142857 | 2.2712485714285715 | 29.187762604899998 | 119.26147007610001 | 1.7972351721000002 | 12.257506592007202 | 10.563954019413515 | 0.7609371428571423 | 1.7682071428571433 | 0.9306385714285714 | 0.5499359251700676 | 0.7234843299319728 | 0.43905424829931977 | 0.579025335379591 | 3.126556500051022 | 0.8660881506306123 | 0.5896706225150046 | 0.9586520619214991 | 0.2611557858821868 | 0.7679001383741277 | 0.9791077887145516 | 0.5110340359332114 |
| user_002 | Walking | 1.446034820521e12 | -3.40991 | -6.89706 | 1.14901 | -4.567182 | -8.556329 | 1.992058 | 11.6274862081 | 47.5694366436 | 1.3202239801000002 | 7.779276755058918 | 9.955637276413874 | 1.1572719999999999 | 1.659269 | 0.843048 | 0.5713082503968251 | 0.9860597781746032 | 0.5245926488095238 | 1.3392784819839996 | 2.753173614361 | 0.710729930304 | 0.5780142333953865 | 1.4819664907472159 | 0.3771716530105871 | 0.7602724731274877 | 1.2173604604829318 | 0.6141430232532054 |
| user_002 | Walking | 1.446034820716e12 | -10.11596 | -7.28026 | 2.03038 | -5.548879090909091 | -8.071053636363636 | 1.3615148181818184 | 102.33264672159999 | 53.0021856676 | 4.1224429444 | 12.62763934128624 | 10.103360417173816 | 4.567080909090908 | 0.7907936363636363 | 0.6688651818181817 | 1.236847335071494 | 1.3437529388364158 | 0.8908252757772531 | 20.858228030182637 | 0.625354575313223 | 0.44738063144866924 | 3.4087432876190964 | 2.2522219045364613 | 1.248465140146506 | 1.8462782259505464 | 1.500740452089055 | 1.1173473677180727 |
| user_002 | Walking | 1.446034821299e12 | -4.25296 | -7.93171 | 2.03038 | -4.890467272727273 | -8.158144545454546 | 1.9189002727272728 | 18.0876687616 | 62.9120235241 | 4.1224429444 | 9.226165792467638 | 10.05839102559979 | 0.637507272727273 | 0.22643454545454578 | 0.11147972727272726 | 2.1731719834710748 | 0.9032194214876031 | 0.9364723223140494 | 0.4064155227801657 | 5.1272603375206754e-2 | 1.242772959280165e-2 | 6.413410629421864 | 1.2099726792833205 | 1.360956890081912 | 2.532471249475868 | 1.099987581422318 | 1.1666005700675413 |
| user_002 | Walking | 1.446034822463e12 | -2.75846 | -8.08499 | 2.6435 | -4.890467272727272 | -7.96306 | 1.1733959090909092 | 7.609101571599999 | 65.36706330009999 | 6.98809225 | 8.942273599130143 | 9.802555864984717 | 2.132007272727272 | 0.12192999999999987 | 1.4701040909090908 | 1.765585041322314 | 1.06378520661157 | 1.399166694214876 | 4.545455010961981 | 1.4866924899999969e-2 | 2.161206038107644 | 5.420495317378587 | 1.9482049458051847 | 3.2649849600306666 | 2.3281957214501077 | 1.3957811238891236 | 1.806926938210471 |
| user_002 | Walking | 1.446034822658e12 | -5.97737 | -8.73643 | 2.37526 | -4.810343636363636 | -8.436838181818182 | 2.040828272727273 | 35.7289521169 | 76.3252091449 | 5.6418600676 | 10.848779716143193 | 10.162650533220145 | 1.1670263636363636 | 0.2995918181818187 | 0.33443172727272685 | 1.6895772727272726 | 0.6666470247933883 | 1.0039288016528927 | 1.3619505334223139 | 8.97552575214879e-2 | 0.11184458020661955 | 5.158998482861532 | 1.165199367313524 | 1.2420907046891458 | 2.2713428809542453 | 1.0794440084198549 | 1.1144912313199893 |
| user_002 | Walking | 1.446034822981e12 | -5.01936 | -7.39522 | 1.37893 | -4.69189909090909 | -8.649341818181817 | 2.0408281818181817 | 25.193974809599997 | 54.6892788484 | 1.9014479449 | 9.043489459434339 | 10.113432651366857 | 0.3274609090909095 | 1.254121818181817 | 0.6618981818181817 | 1.132189256198347 | 0.6324439669421487 | 0.8351294297520663 | 0.10723064698264491 | 1.5728215348396666 | 0.43810920309421475 | 1.8307273614960182 | 0.8123552954916602 | 0.8564678106434943 | 1.353043739683244 | 0.9013075476726355 | 0.9254554611884325 |
| user_002 | Walking | 1.44603482311e12 | -3.06503 | -6.70546 | 0.612526 | -4.552551818181818 | -8.47515909090909 | 1.8770950909090909 | 9.3944089009 | 44.96319381160001 | 0.375188100676 | 7.398161313000414 | 9.872386410211492 | 1.4875218181818175 | 1.7696990909090893 | 1.2645690909090908 | 1.1888786776859503 | 0.9127201652892559 | 0.8566640413223141 | 2.2127211595669403 | 3.131834872364457 | 1.5991349856826442 | 1.9648463503924871 | 1.3822374690904575 | 0.9094341834552345 | 1.401729770816218 | 1.1756859568313545 | 0.9536425868506684 |
| user_002 | Walking | 1.446034824018e12 | -4.75112 | -10.61413 | 1.64717 | -3.5597072727272727 | -8.830492727272729 | 2.190625363636364 | 22.573141254400003 | 112.65975565689999 | 2.7131690089 | 11.745044313249737 | 9.854628189720001 | 1.1914127272727275 | 1.7836372727272707 | 0.5434553636363639 | 1.8286085123966946 | 0.7876243801652887 | 0.9497740661157027 | 1.4194642867074385 | 3.1813619206619763 | 0.29534373226513255 | 4.0277480513314075 | 1.1720520732890294 | 1.2082524051718002 | 2.0069250238440417 | 1.082613538290109 | 1.0992053516844795 |
| user_002 | Walking | 1.446034824601e12 | -5.86241 | -12.49182 | -0.460442 | -5.165676363636364 | -8.82003909090909 | 1.0584344545454545 | 34.3678510081 | 156.0455669124 | 0.21200683536400003 | 13.80671665371112 | 10.55427175573394 | 0.6967336363636356 | 3.671780909090911 | 1.5188764545454545 | 1.386814297520661 | 1.8362090082644626 | 1.0685353719008264 | 0.4854377600404948 | 13.481975044364477 | 2.30698568417257 | 3.6135870597748307 | 4.200258772052517 | 2.26876861299566 | 1.900943728723928 | 2.0494532861357238 | 1.5062432117674955 |
| user_002 | Walking | 1.44603482473e12 | -1.37893 | -7.93171 | 2.52854 | -4.876531818181817 | -8.51696 | 1.0758526363636365 | 1.9014479449 | 62.9120235241 | 6.3935145316 | 8.438423193974097 | 10.201872866422526 | 3.4976018181818174 | 0.5852499999999994 | 1.4526873636363635 | 1.4979750413223138 | 1.6639252066115702 | 1.1828621735537188 | 12.233218478548755 | 0.34251756249999926 | 2.1103005764687683 | 4.416518988749511 | 3.8865774131692716 | 2.4518580528002136 | 2.1015515669974674 | 1.9714404411924982 | 1.5658410049555522 |
| user_002 | Walking | 1.446034825378e12 | -4.21464 | -11.03565 | 1.60885 | -3.556223636363636 | -8.945453636363636 | 2.2533327272727273 | 17.7631903296 | 121.78557092250001 | 2.5883983225 | 11.922128986661738 | 9.95200182983105 | 0.658416363636364 | 2.0901963636363643 | 0.6444827272727274 | 1.5267971074380162 | 0.7100323966942147 | 0.9703596859504132 | 0.43351210790413275 | 4.36892083855868 | 0.4153579857528927 | 3.0702830704655892 | 0.8966544321941399 | 1.1600912409439053 | 1.7522223233555694 | 0.9469183872932978 | 1.0770753181388502 |
| user_002 | Walking | 1.446034825702e12 | -6.36057 | -6.70546 | -4.5224 | -4.221602727272727 | -8.569216363636365 | 1.100239 | 40.4568507249 | 44.96319381160001 | 20.45210176 | 10.289419142813651 | 9.871047406501587 | 2.138967272727273 | 1.863756363636364 | 5.622639 | 1.1765302479338846 | 1.406133223140496 | 1.3836488760330576 | 4.575180993798347 | 3.4735877829950432 | 31.614069324321004 | 1.7407863743604812 | 2.6318653634207365 | 3.892013150556863 | 1.319388636589114 | 1.6223024882618953 | 1.9728185802442308 |
| user_002 | Walking | 1.446034826673e12 | -5.05768 | -8.58315 | 0.191003 | -4.124059999999999 | -9.074347272727273 | 1.838777545454545 | 25.580126982400003 | 73.67046392249999 | 3.6482146009e-2 | 9.96428989195462 | 10.208070843567992 | 0.9336200000000012 | 0.4911972727272733 | 1.647774545454545 | 1.116356280991736 | 0.8408293388429752 | 1.0527011983471075 | 0.8716463044000022 | 0.24127476073471132 | 2.715160952647932 | 1.5012614522227659 | 1.4204436278609316 | 1.490610296004187 | 1.225259748878892 | 1.1918236563606763 | 1.2209055229640773 |
| user_002 | Walking | 1.446034826738e12 | -2.79678 | -6.9737 | 0.919089 | -4.11360909090909 | -9.015125454545453 | 1.7168492727272722 | 7.8219783684 | 48.63249169 | 0.8447245899210001 | 7.569623151010954 | 10.126277190348603 | 1.3168290909090903 | 2.041425454545453 | 0.7977602727272721 | 1.0641013223140499 | 0.9627571900826446 | 0.9700432892561982 | 1.7340388546644612 | 4.16741788646611 | 0.6364214527418915 | 1.3336033649902335 | 1.7547264561511644 | 1.2835722745172367 | 1.1548174595970713 | 1.3246608834532574 | 1.1329484871419515 |
| user_002 | Walking | 1.446034827904e12 | -3.29495 | -7.66346 | -0.11556 | -4.284309090909091 | -8.90364909090909 | 1.5426636363636368 | 10.856695502500001 | 58.728619171599995 | 1.3354113599999998e-2 | 8.342581662033641 | 10.132261341915948 | 0.9893590909090912 | 1.24018909090909 | 1.658223636363637 | 0.9675095867768597 | 1.0663202479338845 | 1.5090592644628098 | 0.9788314107644634 | 1.538068981209915 | 2.749705628195043 | 1.199261063500376 | 1.9615683920033804 | 2.4204171887038566 | 1.0951077862477172 | 1.400560027990011 | 1.55576900235988 |
| user_002 | Walking | 1.446034828033e12 | -2.6435 | -5.74745 | 0.152682 | -4.0752890909090915 | -8.624956363636365 | 1.2186831818181818 | 6.98809225 | 33.0331815025 | 2.3311793124000002e-2 | 6.328079135537418 | 9.750752081507738 | 1.4317890909090916 | 2.877506363636365 | 1.0660011818181818 | 1.0080471074380166 | 1.4938607438016531 | 1.4210179008264463 | 2.050020000846283 | 8.280042872767776 | 1.1363585196377604 | 1.3242157561663412 | 3.201710657197371 | 2.171397846632907 | 1.1507457391475935 | 1.7893324613378507 | 1.4735663699450077 |
| user_002 | Walking | 1.446034829328e12 | -6.09233 | -4.67448 | -2.6447 | -4.590870909090908 | -8.280071818181817 | 0.5149840909090906 | 37.11648482889999 | 21.850763270399998 | 6.994438089999999 | 8.121680010274968 | 9.7659346201661 | 1.5014590909090915 | 3.605591818181817 | 3.1596840909090904 | 1.0327486776859502 | 2.0521945454545447 | 1.4906926363636364 | 2.2543794016735554 | 13.00029235933966 | 9.983603554344006 | 1.479305683744628 | 6.29329290884192 | 2.7833947529780843 | 1.2162671103604783 | 2.5086436392684233 | 1.6683509082258696 |
| user_002 | Walking | 1.446034831531e12 | -5.40257 | -10.23092 | -0.843646 | -4.709316363636363 | -8.746883636363636 | 1.6854949090909093 | 29.187762604899998 | 104.67172404639999 | 0.711738573316 | 11.60048383579823 | 10.21751908316194 | 0.6932536363636368 | 1.4840363636363634 | 2.529140909090909 | 0.8420960330578509 | 1.0194471900826445 | 1.3370945454545453 | 0.48060060433140556 | 2.2023639285950405 | 6.39655373803719 | 1.1584009121630345 | 1.8322100748866275 | 2.1417668458588355 | 1.0762903475192158 | 1.3535915465481556 | 1.4634776547179789 |
| user_002 | Walking | 1.446034832179e12 | -3.02671 | -8.04667 | 2.18366 | -4.834728999999999 | -8.05015 | -4.936900000000008e-2 | 9.1609734241 | 64.7488980889 | 4.768370995600001 | 8.870075676599383 | 9.759479910601112 | 1.8080189999999994 | 3.4799999999997056e-3 | 2.233029 | 1.9616194876033055 | 1.479922231404958 | 2.0582124380165294 | 3.268932704360998 | 1.211039999999795e-5 | 4.9864185148410005 | 6.195976928875267 | 3.318297977098797 | 4.970532145289623 | 2.4891719363827134 | 1.821619602743338 | 2.2294690276587437 |
| user_002 | Walking | 1.446034832374e12 | -4.17632 | -8.31491 | 1.72382 | -4.866081727272727 | -8.025764545454546 | 0.5742078181818182 | 17.441648742399998 | 69.1377283081 | 2.9715553923999996 | 9.463135444602916 | 9.809356874918914 | 0.6897617272727272 | 0.28914545454545326 | 1.1496121818181817 | 1.8115047520661156 | 1.12585652892562 | 1.97998873553719 | 0.47577124041025615 | 8.360509388429678e-2 | 1.32160816858476 | 5.921246149061836 | 2.5081589970130724 | 4.669803684489687 | 2.4333610806992527 | 1.583716829806728 | 2.1609728560279713 |
| user_002 | Walking | 1.446034832892e12 | -2.75846 | -5.78577 | 0.191003 | -4.792922727272727 | -8.670242727272727 | 1.204750909090909 | 7.609101571599999 | 33.475134492900004 | 3.6482146009e-2 | 6.412543817433843 | 10.124198990245487 | 2.0344627272727274 | 2.884472727272727 | 1.013747909090909 | 0.8629973553719009 | 1.2927565289256195 | 1.334877553719008 | 4.139038588661984 | 8.320182914380164 | 1.0276848231861897 | 1.0852627263624255 | 2.697601858566792 | 2.3448212789449783 | 1.0417594378561807 | 1.6424377792071125 | 1.5312809275064385 |
| user_002 | Walking | 1.446034833085e12 | -9.15796 | -5.82409 | -2.98958 | -5.249281818181819 | -7.976992727272727 | 0.2711280909090909 | 83.86823136159998 | 33.9200243281 | 8.937588576400001 | 11.257257404274808 | 9.873436534261957 | 3.90867818181818 | 2.1529027272727266 | 3.260708090909091 | 1.2119984545454545 | 1.962570165289256 | 1.4925924214876034 | 15.277765129021475 | 4.634990153098344 | 10.63221725412001 | 2.480748909105307 | 4.826592534165364 | 3.071682909242764 | 1.5750393357327008 | 2.196950735488933 | 1.7526217245152373 |
| user_002 | Walking | 1.446034833215e12 | -6.62882 | -12.60678 | -0.1922 | -5.82757090909091 | -8.454254545454544 | -0.4465073636363636 | 43.9412545924 | 158.9309019684 | 3.694084e-2 | 14.24461643572055 | 10.675288823021814 | 0.8012490909090904 | 4.152525454545456 | 0.25430736363636364 | 1.6712099256198343 | 2.2244780165289257 | 1.5765175950413224 | 0.6420001056826438 | 17.243467650647947 | 6.467223519967769e-2 | 5.0900214959958845 | 6.285847138249138 | 3.7034431770774034 | 2.2561075984969965 | 2.5071591768870873 | 1.9244332093053798 |
| user_002 | Walking | 1.446034833539e12 | -4.86608 | -7.93171 | 1.80046 | -5.353793636363636 | -7.395220909090909 | 0.19797109090909082 | 23.678734566400003 | 62.9120235241 | 3.2416562115999996 | 9.477996323174008 | 9.547768671452733 | 0.48771363636363585 | 0.5364890909090905 | 1.6024889090909091 | 1.8650285123966943 | 1.6211712396694216 | 1.979673148760331 | 0.23786459109504082 | 0.28782054466446233 | 2.567970703759372 | 5.81993499195815 | 4.597404026959279 | 4.682051222243637 | 2.4124541429751885 | 2.144155784209552 | 2.163804802250803 |
| user_002 | Walking | 1.446034833993e12 | -5.13432 | -10.72909 | -1.03525 | -4.719765454545455 | -8.781721818181817 | 1.6053699999999997 | 26.361241862399996 | 115.11337222809999 | 1.0717425625 | 11.939277894956629 | 10.229185323152004 | 0.4145545454545445 | 1.9473681818181827 | 2.6406199999999997 | 1.0422495041322313 | 0.9972793388429756 | 1.484041785123967 | 0.17185547115702401 | 3.792242835557855 | 6.972873984399999 | 1.6845566543133723 | 1.870692309292413 | 2.9552987618498023 | 1.297904716962448 | 1.3677325430406388 | 1.7190982408954418 |
| user_002 | Walking | 1.446034834964e12 | -5.63249 | -7.62514 | 2.22198 | -5.1621927272727275 | -8.429870000000001 | 0.5324010909090909 | 31.724943600099998 | 58.1427600196 | 4.937195120399999 | 9.736780717470225 | 10.26159981256484 | 0.4702972727272723 | 0.8047300000000011 | 1.6895789090909088 | 1.654741322314049 | 0.785725123966942 | 1.952752859504132 | 0.22117952473471034 | 0.6475903729000017 | 2.8546768900448254 | 4.7563571161466545 | 1.769143344757024 | 4.538077592130551 | 2.1809074065963125 | 1.3300914798452863 | 2.13027641214246 |
| user_002 | Walking | 1.446034835029e12 | -5.47921 | -9.69444 | 1.72382 | -4.914852727272727 | -8.691144545454547 | 1.016631090909091 | 30.021742224100002 | 93.9821669136 | 2.9715553923999996 | 11.268339031556515 | 10.262562882771157 | 0.564357272727273 | 1.0032954545454533 | 0.7071889090909089 | 1.403284132231404 | 0.7594395041322314 | 1.6715264958677687 | 0.3184991312801656 | 1.0066017691115678 | 0.5001161531411898 | 3.776995825254394 | 1.7087985870458295 | 3.2703463178698895 | 1.9434494655777377 | 1.3072102306231501 | 1.8084098865771248 |
| user_003 | Jogging | 1.446047735631e12 | -6.13065 | 1.30349 | -1.41845 | -4.1150026 | -9.376380999999999 | -0.4642744 | 37.5848694225 | 1.6990861801000001 | 2.0120004025 | 6.426192963574935 | 11.322521733313769 | 2.0156473999999998 | 10.679870999999999 | 0.9541756 | 1.508743876269841 | 5.754433644047619 | 0.7064118632539683 | 4.062834441126759 | 114.05964457664096 | 0.91045107563536 | 3.8693449254455743 | 46.70436439108835 | 0.6896023926483791 | 1.9670650536892709 | 6.834059144541284 | 0.8304230203025318 |
| user_003 | Jogging | 1.446047736344e12 | -8.08499 | -2.91174 | -0.996927 | -4.580421818181818 | -9.544642454545455 | -0.9098348181818181 | 65.36706330009999 | 8.4782298276 | 0.993863443329 | 8.65096275399617 | 11.846538256703665 | 3.5045681818181817 | 6.632902454545455 | 8.70921818181819e-2 | 2.3809260991735535 | 7.526616578512397 | 1.4064498429752066 | 12.281998141012396 | 43.995394971515125 | 7.585048133851254e-3 | 7.642918822991907 | 62.51352266285617 | 3.284973150723658 | 2.764582938345657 | 7.906549352458136 | 1.8124494891509826 |
| user_003 | Jogging | 1.446047736408e12 | -6.82042 | 3.29615 | -1.49509 | -4.886984545454545 | -9.18234090909091 | -0.9690569999999998 | 46.51812897640001 | 10.864604822499999 | 2.2352941081 | 7.721271132851119 | 12.219725568362337 | 1.9334354545454557 | 12.478490909090908 | 0.5260330000000002 | 2.501587785123967 | 7.943073272727275 | 1.422918 | 3.738172656893393 | 155.71273536826445 | 0.2767107170890002 | 7.949350319215065 | 70.99922684871673 | 3.299315536658378 | 2.8194592246058576 | 8.426103894963362 | 1.8164018103543 |
| user_003 | Jogging | 1.44604773699e12 | -8.27659 | -19.08292 | 1.30229 | -4.524682272727272 | -8.534378181818182 | -0.2827757272727272 | 68.5019420281 | 364.15783572640004 | 1.6959592440999998 | 20.841202868323126 | 11.605676349077582 | 3.7519077272727284 | 10.54854181818182 | 1.585065727272727 | 2.701741016528926 | 7.784408900826446 | 1.9350175619834713 | 14.07681159396881 | 111.27173448993061 | 2.512433359774619 | 10.161299677030145 | 73.07180543440168 | 6.465091993559316 | 3.1876793560567136 | 8.548204807700953 | 2.5426545171452837 |
| user_003 | Jogging | 1.446047737443e12 | -4.21464 | -13.52647 | -1.22685 | -5.256252636363637 | -9.485420000000001 | 0.15964890909090904 | 17.7631903296 | 182.9653906609 | 1.5051609225 | 14.22089103794133 | 12.227766577150401 | 1.041612636363637 | 4.0410499999999985 | 1.386498909090909 | 2.7387941818181822 | 7.314746694214876 | 2.2672321322314053 | 1.0849568842324064 | 16.33008510249999 | 1.9223792249102807 | 10.071191419435943 | 64.77100283891653 | 6.9486060094820425 | 3.1735140490371148 | 8.048043416813588 | 2.6360208666628653 |
| user_003 | Jogging | 1.446047737832e12 | -4.25296 | -10.65245 | -1.49509 | -4.273859 | -9.380910000000002 | 9.694363636363644e-2 | 18.0876687616 | 113.4746910025 | 2.2352941081 | 11.5670935792964 | 11.598471252113294 | 2.0899e-2 | 1.2715399999999981 | 1.5920336363636365 | 2.398979024793389 | 7.37903520661157 | 2.1345355950413225 | 4.3676820100000004e-4 | 1.6168139715999952 | 2.5345710993132236 | 10.035068711816916 | 65.6950501683973 | 6.309138226020736 | 3.1678176576022987 | 8.105248310101135 | 2.5117997981568387 |
| user_003 | Jogging | 1.446047739644e12 | -4.75112 | -18.58475 | -1.61005 | -3.939425818181818 | -7.938673545454545 | -0.23400472727272728 | 22.573141254400003 | 345.3929325625 | 2.5922610025 | 19.2498918131869 | 10.502220457474333 | 0.8116941818181824 | 10.646076454545454 | 1.3760452727272727 | 2.0211578677685953 | 6.429262074380166 | 1.5872846033057852 | 0.6588474447974886 | 113.33894387602712 | 1.8935005925950743 | 6.706053344980842 | 61.9598330580618 | 3.6706176146924605 | 2.5896048627118464 | 7.871456857409675 | 1.915885595408155 |
| user_003 | Jogging | 1.446047740033e12 | -3.71647 | -18.85299 | 0.497565 | -4.106641636363637 | -8.123307272727272 | -0.9168033636363636 | 13.812149260900002 | 355.4352319400999 | 0.24757092922499999 | 19.22225148441839 | 10.77016093587484 | 0.39017163636363694 | 10.729682727272726 | 1.4143683636363635 | 1.899864553719008 | 6.932174727272727 | 1.5011428016528925 | 0.15223390582267815 | 115.12609142793468 | 2.0004378680554047 | 5.325225943114908 | 67.17508940760895 | 3.137830390156627 | 2.3076451077050186 | 8.19604108137636 | 1.771392218046762 |
| user_003 | Jogging | 1.446047740163e12 | -7.28026 | -6.20729 | -1.34181 | -4.381851636363636 | -9.060413636363636 | -0.8610647272727273 | 53.0021856676 | 38.530449144100004 | 1.8004540760999999 | 9.660905179526399 | 11.655872111868048 | 2.8984083636363644 | 2.8531236363636356 | 0.48074527272727263 | 2.1592396776859504 | 7.111743074380166 | 1.624021338842975 | 8.400771042397228 | 8.140314484376855 | 0.23111601724961975 | 6.355301068903384 | 68.16794305933205 | 3.3226541962235387 | 2.5209722467538955 | 8.256388015308634 | 1.8228149100288649 |
| user_003 | Jogging | 1.446047741586e12 | -5.70913 | -13.79471 | 2.18366 | -4.754604272727273 | -8.084986727272726 | -0.526633 | 32.5941653569 | 190.2940239841 | 4.768370995600001 | 15.088292161030022 | 11.192019763213258 | 0.9545257272727268 | 5.709723272727274 | 2.710293 | 2.1820410909090913 | 7.525347834710744 | 1.5670159504132235 | 0.911119364025528 | 32.60093985112345 | 7.345688145849 | 6.725637535457142 | 70.61233436629162 | 3.209251785542351 | 2.593383414664546 | 8.403114563439654 | 1.7914384682545899 |
| user_003 | Jogging | 1.446047741846e12 | -4.78944 | -17.01362 | 1.26397 | -4.639642454545454 | -8.078019545454545 | -0.5161817272727273 | 22.938735513599998 | 289.4632655044 | 1.5976201609 | 17.720034457610403 | 10.869660757908436 | 0.14979754545454593 | 8.935600454545455 | 1.7801517272727274 | 2.1741234710743806 | 7.074687818181817 | 1.5527638429752066 | 2.2439304624206756e-2 | 79.84495548327294 | 3.168940172112075 | 6.7387556156140995 | 62.02374722341651 | 3.395613878513215 | 2.595911326608461 | 7.875515679840686 | 1.8427191534558962 |
| user_003 | Jogging | 1.446047741911e12 | -9.92436 | -19.08292 | -5.13552 | -4.942721545454545 | -8.123307727272728 | -0.7461035454545454 | 98.4929214096 | 364.15783572640004 | 26.373565670399998 | 22.113894338320424 | 11.071813299428175 | 4.981638454545455 | 10.959612272727274 | 4.389416454545454 | 2.4198804876033058 | 7.12631 | 1.7234634462809917 | 24.816721691806027 | 120.11310116851428 | 19.266976811434386 | 8.522939200738632 | 63.12595207200314 | 4.573635273326528 | 2.9194073372413505 | 7.945184206297745 | 2.1386059181921593 |
| user_003 | Jogging | 1.446047742105e12 | -1.37893 | 1.03525 | -3.71767 | -5.291088181818181 | -8.461222363636365 | -1.038731181818182 | 1.9014479449 | 1.0717425625 | 13.8210702289 | 4.09808012809657 | 11.394511324828654 | 3.912158181818181 | 9.496472363636364 | 2.678938818181818 | 2.4556680578512395 | 7.01831676859504 | 1.8542590495867766 | 15.304981639566938 | 90.18298735330924 | 7.1767131915613955 | 7.66219816374371 | 59.86598085424357 | 4.899174334712138 | 2.7680675865563162 | 7.737310957577159 | 2.213407855482613 |
| user_003 | Jogging | 1.446047743076e12 | -8.27659 | -15.05928 | -0.728685 | -4.587387272727272 | -9.088283272727274 | 0.2606753636363636 | 68.5019420281 | 226.78191411839998 | 0.530981829225 | 17.19926853025224 | 11.999853736250367 | 3.689202727272728 | 5.970996727272725 | 0.9893603636363637 | 3.0143207438016533 | 7.571903148760328 | 1.6436561157024796 | 13.610216762916535 | 35.65280191710159 | 0.9788339291346778 | 13.052084914282272 | 67.81846212605052 | 3.7920228341500035 | 3.612766933291196 | 8.235196544469993 | 1.9473116941440072 |
| user_003 | Jogging | 1.44604774327e12 | -2.29862 | -1.18733 | 1.30229 | -4.639641818181818 | -8.698112363636362 | -2.150190909090909e-2 | 5.2836539044 | 1.4097525289 | 1.6959592440999998 | 2.8964401732816785 | 11.391576321113966 | 2.3410218181818183 | 7.510782363636362 | 1.323791909090909 | 2.8230356198347106 | 7.835078702479337 | 1.5758826859504131 | 5.480383153203307 | 56.41185171391101 | 1.7524250185745534 | 12.127020067467846 | 69.08815603044107 | 2.9333404785628705 | 3.4823871219994835 | 8.311928538578822 | 1.7126997631116991 |
| user_003 | Jogging | 1.446047743659e12 | -6.28393 | -8.54483 | -3.90927 | -4.674479090909092 | -8.569216363636365 | -0.5161813636363636 | 39.4877762449 | 73.01411972889998 | 15.282391932899998 | 11.304171261384 | 11.09202422756434 | 1.6094509090909082 | 2.438636363636526e-2 | 3.393088636363636 | 2.6973064462809915 | 7.171282801652893 | 1.653155925619835 | 2.590332228773551 | 5.946947314050379e-4 | 11.513050494220039 | 11.415731180268821 | 63.780162821143975 | 3.500501430448313 | 3.3787173868598157 | 7.9862483570913305 | 1.8709627015117947 |
| user_003 | Jogging | 1.446047744112e12 | -6.32225 | -19.58108 | -2.2615 | -4.158896818181819 | -8.503027454545455 | -0.5684371818181818 | 39.970845062500004 | 383.4186939664 | 5.114382249999999 | 20.700336260044182 | 11.354514333727545 | 2.1633531818181817 | 11.078052545454545 | 1.6930628181818181 | 2.7714127272727276 | 6.852686305785125 | 1.8821283719008262 | 4.680096989282851 | 122.72324819985192 | 2.86646170630976 | 12.191477094626935 | 62.46514659401501 | 5.505646912405573 | 3.4916295758036724 | 7.903489520080039 | 2.346411496819254 |
| user_003 | Jogging | 1.446047745795e12 | -1.11069 | 0.958607 | -2.68302 | -3.9951649999999996 | -9.670054818181818 | -9.465881818181819e-2 | 1.2336322760999998 | 0.918927380449 | 7.1985963204 | 3.0579659868855638 | 11.578644378470724 | 2.8844749999999997 | 10.628661818181818 | 2.5883611818181818 | 2.4385655123966945 | 6.973345719008264 | 1.5590983388429753 | 8.320196025624998 | 112.96845204527604 | 6.699613607543214 | 8.35464690663406 | 56.06450086026842 | 3.6200956105588475 | 2.8904406076987743 | 7.487623178303541 | 1.9026548847751785 |
| user_003 | Jogging | 1.446047746378e12 | -8.23827 | -17.32018 | -1.26517 | -4.576936636363637 | -9.276400272727273 | -0.4743760909090911 | 67.8690925929 | 299.98863523240004 | 1.6006551288999997 | 19.221300240987862 | 11.717208326426968 | 3.6613333636363627 | 8.043779727272728 | 0.7907939090909089 | 2.3983437355371895 | 6.6256129008264475 | 2.0360425371900828 | 13.405361999676762 | 64.70239230088373 | 0.6253550066552807 | 7.549598737169998 | 57.38207630334404 | 5.904934442457743 | 2.747653314588651 | 7.575095794994545 | 2.4300070869151273 |
| user_003 | Jogging | 1.446047746443e12 | -1.53221 | -8.12331 | 1.72382 | -4.806858181818182 | -9.607348454545455 | -0.282774090909091 | 2.3476674841 | 65.9881653561 | 2.9715553923999996 | 8.4443702093525 | 12.065932595475562 | 3.274648181818182 | 1.4840384545454555 | 2.0065940909090907 | 2.191540991735537 | 6.272179206611571 | 2.185207082644628 | 10.723320714685125 | 2.202370134569664 | 4.02641984567128 | 5.724745155916998 | 54.95898877838006 | 6.258809155594935 | 2.3926439676468787 | 7.413432995473828 | 2.5017612107463285 |
| user_003 | Jogging | 1.446047747867e12 | -8.54483 | -16.05561 | -3.25783 | -4.4793936363636355 | -8.879263090909092 | -0.8645490000000001 | 73.01411972889998 | 257.7826124721 | 10.613456308899998 | 18.477288451228443 | 11.952809629959383 | 4.065436363636364 | 7.17634690909091 | 2.3932809999999995 | 2.48765438016529 | 7.61592423140496 | 1.7449996942148758 | 16.52777282677686 | 51.499954959618655 | 5.727793944960998 | 9.127936903276487 | 72.93491111289995 | 4.231126364306643 | 3.0212475739794127 | 8.540193856868822 | 2.056970190427329 |
| user_003 | Jogging | 1.446047749615e12 | -5.24928 | -13.29655 | 1.22565 | -4.2076685454545455 | -8.370647363636364 | -0.29670981818181813 | 27.5549405184 | 176.7982419025 | 1.5022179224999999 | 14.347661842383935 | 11.022203270846463 | 1.0416114545454542 | 4.925902636363636 | 1.522359818181818 | 1.9812563471074378 | 7.141195123966941 | 1.5100093719008265 | 1.084954422240297 | 24.264516782934223 | 2.317579416014578 | 6.159065103561868 | 61.365181331507394 | 3.5171502368635177 | 2.4817463817968726 | 7.833593130327065 | 1.8754066857253968 |
| user_003 | Jogging | 1.446047750068e12 | -8.12331 | -17.09026 | -4.40743 | -4.510747636363636 | -8.475158181818182 | -0.7251994545454544 | 65.9881653561 | 292.0769868676 | 19.425439204899998 | 19.429117103682298 | 11.165998374535848 | 3.612562363636364 | 8.615101818181818 | 3.682230545454545 | 2.161772330578512 | 7.3682685950413225 | 1.7811014214876033 | 13.050606831161954 | 74.21997933763967 | 13.558821789878477 | 6.444378643861114 | 63.33591248530026 | 3.728135679427015 | 2.5385780751950717 | 7.958386299074723 | 1.9308380769570024 |
| user_003 | Jogging | 1.446047750974e12 | 3.8919e-2 | -0.842448 | -1.57173 | -3.8732365454545454 | -9.973134363636364 | -0.40122081818181826 | 1.514688561e-3 | 0.7097186327039999 | 2.4703351929000004 | 1.7836951853287601 | 11.766917856584998 | 3.9121555454545454 | 9.130686363636364 | 1.1705091818181819 | 2.1823563719008265 | 6.663935347107437 | 1.389666223140496 | 15.304961011830752 | 83.36943347109505 | 1.3700917447206695 | 6.276925041612132 | 57.1699286394013 | 3.746889708828522 | 2.5053792211184582 | 7.561079859345575 | 1.9356884327878086 |
| user_003 | Jogging | 1.446047751169e12 | -2.6435 | -19.58108 | 2.10702 | -3.7025366363636367 | -8.384583090909091 | -0.17478227272727276 | 6.98809225 | 383.4186939664 | 4.439533280399999 | 19.870740285575675 | 10.519545204986324 | 1.0590366363636368 | 11.196496909090909 | 2.2818022727272727 | 1.7560833388429749 | 6.586661157024793 | 1.5315473223140499 | 1.1215585971604058 | 125.36154303528228 | 5.206621611823347 | 4.519674473767474 | 57.41535496920656 | 4.058250858479553 | 2.1259526038384475 | 7.577292060439967 | 2.0145100790215853 |
| user_003 | Jogging | 1.446047751428e12 | -4.02303 | -2.79678 | 1.41725 | -4.137994636363636 | -8.785204 | -0.44999136363636366 | 16.1847703809 | 7.8219783684 | 2.0085975625 | 5.100524121284008 | 10.925348616610346 | 0.11496463636363607 | 5.988424 | 1.8672413636363636 | 1.6297226611570246 | 7.185217322314048 | 1.6297223140495867 | 1.3216867614223074e-2 | 35.861222003776 | 3.4865903100745865 | 5.189255505654557 | 62.7452396789755 | 3.459631785679336 | 2.277993745745268 | 7.921189284379935 | 1.86000854451783 |
| user_003 | Jumping | 1.446047778488e12 | 0.805325 | -10.92069 | -1.49509 | -3.045865625 | -10.45605625 | -2.8985725 | 0.6485483556249999 | 119.26147007610001 | 2.2352941081 | 11.051937049215626 | 11.40789343159099 | 3.8511906249999996 | 0.46463375000000084 | 1.4034825000000002 | 1.2694057180059524 | 2.5898877395833337 | 0.9794885 | 14.831669230087888 | 0.2158845216390633 | 1.9697631278062506 | 2.994031096828895 | 19.251323506625305 | 2.225648644605465 | 1.7303268757170984 | 4.387633018681633 | 1.491860799339357 |
| user_003 | Jumping | 1.446047779394e12 | -3.75479 | -19.19788 | -4.9056 | -2.7758821818181816 | -10.80573090909091 | -2.442647 | 14.098447944099998 | 368.55859649440004 | 24.064911359999996 | 20.167348754819013 | 11.820595137903576 | 0.9789078181818183 | 8.392149090909092 | 2.4629529999999997 | 1.600585561983471 | 5.376877752066116 | 1.2062982314049586 | 0.9582605164974878 | 70.42816636404629 | 6.066137480208998 | 3.2206154865366234 | 39.98863645901631 | 1.938698124881656 | 1.7946073349166451 | 6.323656889728942 | 1.3923714033553174 |
| user_003 | Jumping | 1.446047779718e12 | -0.574206 | -0.535886 | -1.18853 | -2.692273545454545 | -6.2699988181818185 | -1.8852610909090906 | 0.329712530436 | 0.287173804996 | 1.4126035609000003 | 1.4246016623365285 | 7.945104724247008 | 2.1180675454545446 | 5.734112818181819 | 0.6967310909090905 | 1.9156986446280988 | 6.127132917355372 | 1.4292522892561985 | 4.48621012710784 | 32.88004981163704 | 0.4854342130393713 | 4.538447186411323 | 48.73007696745106 | 2.676119181954217 | 2.130363158339752 | 6.980693158093333 | 1.6358848315068566 |
| user_003 | Jumping | 1.446047779783e12 | -1.72382 | -13.98632 | -4.10087 | -2.5633780909090906 | -7.144398818181816 | -2.104732 | 2.9715553923999996 | 195.61714714239997 | 16.817134756899996 | 14.676710710908626 | 8.766751727191796 | 0.8395580909090907 | 6.841921181818183 | 1.9961379999999997 | 1.8707269752066111 | 6.4061433140495865 | 1.5936175289256198 | 0.704857788010917 | 46.81188545821232 | 3.984566915043999 | 4.440687567006215 | 51.69169643753226 | 3.035135331393246 | 2.107293896685086 | 7.189693765212275 | 1.7421639794787533 |
| user_003 | Jumping | 1.446047779847e12 | -3.14167 | -19.58108 | -3.48775 | -2.5424762727272725 | -8.530896999999998 | -2.1987910909090904 | 9.8700903889 | 383.4186939664 | 12.1644000625 | 20.135868106883297 | 10.050840976414916 | 0.5991937272727275 | 11.050183000000002 | 1.2889589090909097 | 1.7627335867768592 | 7.189334537190083 | 1.6230705206611569 | 0.3590331228029837 | 122.10654433348905 | 1.661415069324828 | 4.182981353748214 | 62.25323570487753 | 3.1015204576431388 | 2.0452338139558064 | 7.890071970830021 | 1.7611134141909028 |
| user_003 | Jumping | 1.446047780236e12 | -4.02303 | -9.84772 | -0.383802 | -2.0791488181818183 | -7.405672454545454 | -1.9200985454545454 | 16.1847703809 | 96.97758919840001 | 0.147303975204 | 10.644701196111802 | 8.261996395467271 | 1.943881181818182 | 2.442047545454547 | 1.5362965454545454 | 1.4647220413223139 | 4.597488347107438 | 1.317141173553719 | 3.778674049026852 | 5.963596214260577 | 2.36020707557557 | 2.7032977327714196 | 31.28153946209859 | 2.150969664053501 | 1.6441708344242758 | 5.5929902075811455 | 1.4666184452861286 |
| user_003 | Jumping | 1.446047780559e12 | -1.8771 | 1.99325 | -1.68669 | -2.995352090909091 | -8.844426363636364 | -2.5123207272727273 | 3.52350441 | 3.9730455625 | 2.8449231561 | 3.215816090605929 | 10.297818465970156 | 1.118252090909091 | 10.837676363636364 | 0.8256307272727272 | 1.4400184710743802 | 4.84324438016529 | 1.320624966942149 | 1.2504877388225537 | 117.45522896292232 | 0.6816660978168925 | 2.8856324564354807 | 37.52954362327869 | 2.3031833074215906 | 1.6987149426656258 | 6.126136108778411 | 1.5176242312975865 |
| user_003 | Jumping | 1.446047782114e12 | -3.02671 | -6.93538 | -2.68302 | -2.1592739090909094 | -9.077831363636363 | -2.784047363636363 | 9.1609734241 | 48.0994957444 | 7.1985963204 | 8.028640326288132 | 10.440987932571106 | 0.8674360909090906 | 2.142451363636363 | 0.101027363636363 | 1.4593388842975203 | 6.418493917355372 | 1.5176108347107438 | 0.7524453718116441 | 4.590097845547312 | 1.020652820331392e-2 | 3.096061633576934 | 52.39564747111347 | 2.9478029008267157 | 1.7595629098094032 | 7.23848378261038 | 1.7169166842997117 |
| user_003 | Jumping | 1.446047783021e12 | -1.76214 | -12.22358 | -5.67201 | -2.2951368181818177 | -8.046666363636364 | -2.6028974545454546 | 3.1051373796000004 | 149.4159080164 | 32.171697440100004 | 13.590170817031698 | 9.340850426104161 | 0.5329968181818177 | 4.176913636363636 | 3.0691125454545456 | 1.147392132231405 | 5.408547049586778 | 1.5980530743801653 | 0.28408560819194156 | 17.446607525640495 | 9.41945181666648 | 2.104757384422643 | 39.50631767277085 | 3.717980372110848 | 1.450778199595873 | 6.285405131952183 | 1.9282065169765525 |
| user_003 | Jumping | 1.446047783474e12 | -2.8351 | -4.63616 | -3.75599 | -1.9258677272727274 | -6.823900909090909 | -2.5610926363636364 | 8.03779201 | 21.493979545600002 | 14.107460880100001 | 6.6059997302225195 | 8.171110182376047 | 0.9092322727272728 | 2.187740909090909 | 1.1948973636363638 | 1.018495512396694 | 4.917666958677686 | 1.2648870661157026 | 0.8267033257688018 | 4.786210285309917 | 1.4277797096251326 | 1.6454297082086164 | 35.01551621607166 | 2.5264965598920623 | 1.2827430406003442 | 5.917390997396713 | 1.589495693574557 |
| user_003 | Jumping | 1.446047783928e12 | -1.22565 | 0.345482 | -0.805325 | -2.3752596363636367 | -6.256064 | -2.17789 | 1.5022179224999999 | 0.119357812324 | 0.6485483556249999 | 1.5066930976310338 | 7.873185761106638 | 1.1496096363636368 | 6.601546 | 1.3725650000000003 | 1.3497614297520661 | 5.791750727272727 | 1.7095292975206613 | 1.3216023160201331 | 43.580409590116 | 1.8839346792250007 | 2.7612193103155254 | 44.96614526686418 | 5.186450031648668 | 1.6616917013440025 | 6.705680074896518 | 2.2773778851232986 |
| user_003 | Jumping | 1.446047785158e12 | -4.3296 | -19.38948 | -5.17384 | -2.127920545454545 | -8.778236090909092 | -3.0174524545454546 | 18.74543616 | 375.95193467039996 | 26.768620345600002 | 20.529636898299003 | 10.425059342349634 | 2.201679454545455 | 10.611243909090907 | 2.1563875454545456 | 1.4099344958677689 | 7.0499873553719 | 1.8989135371900827 | 4.847392420567572 | 112.59849729821889 | 4.65000724619148 | 2.826879506975875 | 61.40943646769882 | 5.966533630196475 | 1.6813326580352488 | 7.836417323477535 | 2.442648896218299 |
| user_003 | Jumping | 1.446047785741e12 | -5.67081 | -19.58108 | -6.59169 | -2.981417909090909 | -11.300410000000001 | -3.0104854545454547 | 32.158086056100004 | 383.4186939664 | 43.450377056099995 | 21.424919068192533 | 12.213006242934197 | 2.689392090909091 | 8.280669999999999 | 3.581204545454545 | 1.1746283057851243 | 4.157596 | 1.0412976611570246 | 7.232829818644373 | 68.56949564889999 | 12.825025996384296 | 1.990508293795564 | 32.482416710883314 | 2.1246119344821945 | 1.4108537464229112 | 5.699334760380664 | 1.457604862259383 |
| user_003 | Jumping | 1.446047786065e12 | -3.7722e-2 | 0.230521 | 0.382604 | -2.6016978181818184 | -7.217555818181818 | -2.3207198181818183 | 1.422949284e-3 | 5.3139931441e-2 | 0.146385820816 | 0.4482730212058272 | 8.746920955704528 | 2.5639758181818184 | 7.448076818181819 | 2.7033238181818184 | 1.274704181818182 | 5.073483123966943 | 1.6246536198347108 | 6.573971996221125 | 55.4738482895374 | 7.3079596659491255 | 2.612508774391919 | 37.036034654309766 | 4.703861187725261 | 1.6163257018286628 | 6.085723839799976 | 2.1688386725907627 |
| user_003 | Jumping | 1.44604778697e12 | -0.267643 | -8.92803 | -1.57173 | -2.608665363636364 | -10.115963636363636 | -3.3309809090909095 | 7.163277544900001e-2 | 79.7097196809 | 2.4703351929000004 | 9.069271616246201 | 11.16523610158163 | 2.341022363636364 | 1.1879336363636366 | 1.7592509090909094 | 1.3396285206611571 | 3.1194631487603317 | 1.5305941487603305 | 5.4803857070455875 | 1.4111863244041327 | 3.0949637611371914 | 2.725388990184014 | 16.98034189431018 | 4.198812068786995 | 1.6508752194469496 | 4.120721040583818 | 2.0491003071560443 |
| user_003 | Jumping | 1.446047787165e12 | -0.650847 | 0.383802 | -0.728685 | -2.3682926363636363 | -7.3185804545454545 | -2.703921636363636 | 0.42360181740899994 | 0.147303975204 | 0.530981829225 | 1.049708350847034 | 9.016998308071146 | 1.7174456363636363 | 7.702382454545455 | 1.9752366363636358 | 1.3750980991735537 | 5.015211049586777 | 1.9258318429752064 | 2.9496195138644956 | 59.32669547608966 | 3.90155976963313 | 3.0779925111574262 | 38.28173155279879 | 5.6355475934485995 | 1.7544208477892145 | 6.187223250602712 | 2.373930831647923 |
| user_003 | Jumping | 1.446047788071e12 | -0.382604 | -7.1653 | -1.83997 | -2.674855818181818 | -10.45388 | -3.4598772727272733 | 0.146385820816 | 51.34152409 | 3.3854896009 | 7.407658166500125 | 11.447674154518019 | 2.2922518181818177 | 3.2885799999999996 | 1.6199072727272732 | 0.9424898099173553 | 3.4890507603305783 | 1.4162686033057852 | 5.254418397957849 | 10.814758416399998 | 2.6240995722347122 | 1.5625571828485925 | 19.114791200640433 | 3.1594544490391656 | 1.2500228729301686 | 4.372046568901163 | 1.7774854286432746 |
| user_003 | Jumping | 1.446047788524e12 | -1.83878 | -13.06663 | -4.40743 | -2.284686363636364 | -9.189309181818182 | -3.341432 | 3.3811118884000004 | 170.7368195569 | 19.425439204899998 | 13.911986581728721 | 10.958216505306934 | 0.4459063636363638 | 3.8773208181818184 | 1.0659979999999996 | 1.2341684628099174 | 7.045554041322314 | 2.3818761983471077 | 0.1988324851314051 | 15.033616727106125 | 1.136351736003999 | 2.4039647392245826 | 56.751996420586586 | 6.585076787756369 | 1.5504724245289185 | 7.533392092582636 | 2.566140445836192 |
| user_003 | Jumping | 1.446047788978e12 | -4.98104 | -15.97897 | -4.67568 | -2.476287272727273 | -9.502837545454547 | -3.463361818181818 | 24.8107594816 | 255.3274822609 | 21.861983462399998 | 17.37815367652444 | 10.622998855950827 | 2.5047527272727272 | 6.4761324545454535 | 1.2123181818181816 | 0.6900824132231407 | 3.6885678677685947 | 1.235749785123967 | 6.2737862247801655 | 41.94029156881692 | 1.4697153739669417 | 0.9094025823986598 | 24.75910834947861 | 2.8048255856699282 | 0.9536260181007331 | 4.975852524892454 | 1.6747613518558184 |
| user_003 | Jumping | 1.446047789366e12 | -0.689167 | 0.230521 | -0.728685 | -2.040828272727273 | -7.03640390909091 | -2.648185181818182 | 0.47495115388899994 | 5.3139931441e-2 | 0.530981829225 | 1.029112683118326 | 8.566401817896816 | 1.3516612727272732 | 7.2669249090909105 | 1.919500181818182 | 0.9640250082644628 | 5.1925611652892565 | 1.7240976033057849 | 1.826988196190712 | 52.80819763436594 | 3.684480948000034 | 1.8265146617737733 | 38.92138095737842 | 4.682598069418348 | 1.3514860938144253 | 6.238700261863718 | 2.163931160970318 |
| user_003 | Jumping | 1.446047789431e12 | -1.14901 | -1.95374 | -2.72134 | -1.953736454545455 | -6.5486911818181825 | -2.7039233636363638 | 1.3202239801000002 | 3.8170999876000002 | 7.405691395600001 | 3.5416119724357156 | 8.169979126377969 | 0.8047264545454549 | 4.594951181818183 | 1.741663636363633e-2 | 1.0083625371900826 | 5.557712355371901 | 1.6746931487603307 | 0.647584666645298 | 21.113576363292314 | 3.0333922222313927e-4 | 1.8762498455014827 | 40.81039543439705 | 4.654028349160952 | 1.3697626967841847 | 6.388301451434258 | 2.1573197141733425 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 | -2.1139846363636363 | -7.62514390909091 | -3.205570636363637 | 15.2730111249 | 314.7682737241 | 57.578047520400006 | 19.688050496923253 | 9.358800088769957 | 1.7940853636363636 | 10.116566090909092 | 4.382449363636363 | 1.1537262148760332 | 6.411527363636364 | 2.0167259669421482 | 3.2187422920142232 | 102.34490947173167 | 19.205862424836763 | 2.1654029518886837 | 50.06674660371802 | 6.36506042608091 | 1.4715308192113015 | 7.0757859354080255 | 2.5229071378235286 |
| user_003 | Jumping | 1.44604778969e12 | -2.79678 | -7.7401 | -2.37646 | -2.023410090909091 | -8.565733909090909 | -3.4076233636363638 | 7.8219783684 | 59.90914801 | 5.647562131599999 | 8.566136148229258 | 10.261278348407382 | 0.773369909090909 | 0.8256339090909091 | 1.031163363636364 | 1.00804579338843 | 6.803598520661157 | 2.0537784710743803 | 0.5981010162872809 | 0.6816713518407355 | 1.06329788250586 | 1.7002829353981377 | 55.666831209060064 | 6.432113881496044 | 1.303948977298628 | 7.46102078867631 | 2.5361612491117445 |
| user_003 | Jumping | 1.446047790078e12 | -5.51753 | -19.58108 | -7.24314 | -2.786332727272727 | -10.19608909090909 | -3.6131590909090905 | 30.4431373009 | 383.4186939664 | 52.4630770596 | 21.594557377424987 | 11.293414580744383 | 2.7311972727272726 | 9.38499090909091 | 3.62998090909091 | 0.8785175041322314 | 4.163930198347109 | 1.2959231239669422 | 7.459438542552892 | 88.07805436371902 | 13.176761400364468 | 1.3735754555330413 | 30.66790812930183 | 3.5983363200454987 | 1.171996354743922 | 5.537861331714783 | 1.8969281272745941 |
| user_003 | Jumping | 1.446047790984e12 | -3.94639 | -5.36425 | -3.75599 | -2.120953181818182 | -7.109561 | -3.2508581818181814 | 15.5739940321 | 28.7751780625 | 14.107460880100001 | 7.645693753656368 | 8.673083640943078 | 1.8254368181818181 | 1.745311 | 0.5051318181818187 | 1.195847074380165 | 4.592418834710744 | 1.636056132231405 | 3.33221957717376 | 3.046110486721 | 0.25515815373967 | 2.4076508899487554 | 32.64328227998147 | 4.830573809957643 | 1.551660687762874 | 5.713429992568516 | 2.1978566399921635 |
| user_003 | Jumping | 1.446047791114e12 | -7.70178 | -19.58108 | -7.5497 | -2.946581363636364 | -10.105513 | -4.309891818181819 | 59.317415168400004 | 383.4186939664 | 56.997970089999995 | 22.354732814882848 | 11.61773381092931 | 4.7551986363636365 | 9.475567 | 3.239808181818181 | 1.3019401157024795 | 4.772619710743801 | 1.4381207768595041 | 22.611914071274587 | 89.786369971489 | 10.496357054976027 | 3.343503517246846 | 35.837988853437764 | 4.02036337652106 | 1.8285249566923734 | 5.9864838472543935 | 2.0050843813967183 |
| user_003 | Jumping | 1.446047791568e12 | -2.87342 | -15.97897 | -6.28513 | -2.3055863636363636 | -6.6601691818181825 | -3.212538181818182 | 8.2565424964 | 255.3274822609 | 39.5028591169 | 17.409390680727455 | 8.421526987651102 | 0.5678336363636363 | 9.318800818181817 | 3.0725918181818175 | 1.446669702479339 | 6.484367231404959 | 2.031925909090909 | 0.3224350385859503 | 86.8400486889461 | 9.440820481157846 | 3.813957346986096 | 49.52847432526871 | 5.995280938105451 | 1.9529355716423664 | 7.037646930989697 | 2.448526278826807 |
| user_003 | Jumping | 1.446047791827e12 | -2.29862 | -6.55217 | -2.49142 | -1.9641881818181817 | -7.280260090909092 | -3.181184545454545 | 5.2836539044 | 42.930931708900005 | 6.207173616400001 | 7.377110493255472 | 8.788531724429772 | 0.33443181818181844 | 0.7280900909090917 | 0.6897645454545449 | 1.1261739999999998 | 6.306384099173553 | 2.2213108677685947 | 0.11184464101239687 | 0.5301151804800094 | 0.47577512816611495 | 1.9109248881675382 | 50.99588928979224 | 6.629910003092078 | 1.3823620684059361 | 7.141140615461387 | 2.5748611619060315 |
| user_003 | Jumping | 1.446047792344e12 | -3.10335 | 3.14286 | -1.26517 | -2.953548181818182 | -8.90713090909091 | -3.501681181818181 | 9.6307812225 | 9.877568979600001 | 1.6006551288999997 | 4.594453757629953 | 10.941628346937778 | 0.14980181818181793 | 12.04999090909091 | 2.236511181818181 | 0.999812826446281 | 3.8237974793388427 | 1.812455851239669 | 2.2440584730578434e-2 | 145.2022809091736 | 5.001982266397757 | 1.8885143120312426 | 26.270304023665425 | 5.8452978576593555 | 1.374232262767558 | 5.12545646978544 | 2.4177050807861895 |
| user_003 | Jumping | 1.446047793057e12 | -3.40991 | -4.5212 | -3.18118 | -2.462351909090909 | -7.2071046363636375 | -3.024419818181818 | 11.6274862081 | 20.441249440000004 | 10.1199061924 | 6.495278426711207 | 8.674293689934707 | 0.9475580909090908 | 2.685904636363637 | 0.15676018181818208 | 1.0726530165289256 | 4.93793579338843 | 1.4678901570247935 | 0.8978663356472808 | 7.214083715639682 | 2.4573754603669503e-2 | 1.72976303665992 | 39.52732881159241 | 3.1747400768519607 | 1.3152045607660885 | 6.287076332572431 | 1.7817800304336 |
| user_003 | Jumping | 1.446047793186e12 | -4.7128 | -14.7144 | -2.75966 | -3.2113400000000003 | -8.840943 | -3.634060909090908 | 22.21048384 | 216.51356736 | 7.615723315599999 | 15.695215019731332 | 10.612367757126883 | 1.5014599999999994 | 5.873457 | 0.8744009090909084 | 0.7952270578512397 | 4.540798404958678 | 1.071702603305785 | 2.254382131599998 | 34.497497130849 | 0.764576949819007 | 0.7830567086205332 | 34.99927923490504 | 1.741114685615601 | 0.8849049150165984 | 5.916018867017332 | 1.3195130486719717 |
| user_003 | Jumping | 1.446047793575e12 | -0.995729 | 0.383802 | -0.537083 | -2.7410467272727272 | -6.395409181818182 | -2.8885566363636364 | 0.991476241441 | 0.147303975204 | 0.28845814888899995 | 1.1946708189011732 | 8.27717575135295 | 1.7453177272727274 | 6.779211181818182 | 2.3514736363636364 | 1.2639381239669418 | 5.466502917355373 | 1.8156223140495868 | 3.0461339691324385 | 45.957704247688675 | 5.529428262513223 | 1.9598808640562646 | 39.03616815904957 | 4.99167780265026 | 1.3999574508020822 | 6.247893097600948 | 2.2342063026162693 |
| user_003 | Jumping | 1.446047794481e12 | -1.95374 | 3.41111 | -1.07357 | -2.643502818181818 | -9.126602727272727 | -3.428524545454545 | 3.8170999876000002 | 11.635671432099999 | 1.1525525448999998 | 4.0749630629737 | 10.79227622301754 | 0.6897628181818181 | 12.537712727272726 | 2.354954545454545 | 1.1131894545454544 | 3.9501590082644626 | 1.6290873471074379 | 0.4757727453461238 | 157.19424043161652 | 5.545810911157022 | 1.8705564207425525 | 31.301814921364322 | 3.736381640676486 | 1.3676828655585886 | 5.594802491720715 | 1.932972229670278 |
| user_003 | Sitting | 1.44604752743e12 | 0.460442 | -0.689167 | 9.38788 | 0.46044236363636365 | -0.657814 | 9.443618181818183 | 0.21200683536400003 | 0.47495115388899994 | 88.13229089439999 | 9.424396473178163 | 9.478071981030691 | 3.6363636363168084e-7 | 3.1352999999999964e-2 | 5.573818181818346e-2 | 3.4032538370720185e-2 | 4.40547175127902e-2 | 2.419795146267877e-2 | 1.3223140495527202e-13 | 9.830106089999977e-4 | 3.1067449123968775e-3 | 2.3483069980241396e-3 | 3.906631446102497e-3 | 8.135460878653428e-4 | 4.845933344593319e-2 | 6.250305149432703e-2 | 2.8522729320058816e-2 |
| user_003 | Sitting | 1.446047527559e12 | 0.460442 | -0.612526 | 9.50284 | 0.453475 | -0.6578139090909091 | 9.44710181818182 | 0.21200683536400003 | 0.375188100676 | 90.30396806560002 | 9.53368569870226 | 9.481220909788886 | 6.967000000000001e-3 | 4.528790909090907e-2 | 5.573818181818169e-2 | 3.4032455037386854e-2 | 5.070537866981499e-2 | 2.7628805457168906e-2 | 4.8539089000000014e-5 | 2.0509947098264446e-3 | 3.1067449123966797e-3 | 2.308586652947751e-3 | 4.1636947314954344e-3 | 1.065423617408702e-3 | 4.804775388036106e-2 | 6.452669781954935e-2 | 3.264082746207121e-2 |
| user_003 | Sitting | 1.446047528402e12 | 0.422122 | -0.689167 | 9.46452 | 0.4569586363636363 | -0.6752324545454544 | 9.457552727272725 | 0.178186982884 | 0.47495115388899994 | 89.5771388304 | 9.498961888920968 | 9.492741447006507 | 3.483663636363632e-2 | 1.3934545454545533e-2 | 6.967272727274931e-3 | 3.166966115702479e-2 | 2.5652355371900804e-2 | 2.565223140495896e-2 | 1.2135912331322283e-3 | 1.9417155702479557e-4 | 4.854288925622906e-5 | 1.4210099365206607e-3 | 7.910343762411707e-4 | 1.09331825574758e-3 | 3.769628544725144e-2 | 2.8125333353423043e-2 | 3.3065363384478026e-2 |
| user_003 | Sitting | 1.446047529049e12 | 0.460442 | -0.650847 | 9.46452 | 0.4639258181818182 | -0.657814 | 9.436650909090908 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.471062812864579 | 3.483818181818199e-3 | 6.9670000000000565e-3 | 2.786909090909262e-2 | 2.0901991735537175e-2 | 3.3886504132231404e-2 | 2.565223140495896e-2 | 1.2136989123967062e-5 | 4.853908900000079e-5 | 7.766862280992689e-4 | 1.0150072620518401e-3 | 1.7045471308392188e-3 | 1.0182974268970488e-3 | 3.1859178615460886e-2 | 4.128616149315917e-2 | 3.191077289720587e-2 |
| user_003 | Sitting | 1.446047529689e12 | 0.383802 | -0.689167 | 9.50284 | 0.45347518181818175 | -0.7031015454545454 | 9.45406909090909 | 0.147303975204 | 0.47495115388899994 | 90.30396806560002 | 9.53552427476817 | 9.4913909847906 | 6.967318181818177e-2 | 1.3934545454545422e-2 | 4.877090909091031e-2 | 4.687115702479338e-2 | 4.972128099173549e-2 | 4.6237355371901406e-2 | 4.854352264669415e-3 | 1.9417155702479248e-4 | 2.378601573553838e-3 | 3.2568331531720477e-3 | 3.7676414018760294e-3 | 2.8993343855747643e-3 | 5.7068670504682756e-2 | 6.138111600383321e-2 | 5.3845467641898744e-2 |
| user_003 | Sitting | 1.44604752981e12 | 0.422122 | -0.765807 | 9.54116 | 0.4325731818181817 | -0.710068909090909 | 9.499356363636364 | 0.178186982884 | 0.586460361249 | 91.0337341456 | 9.58114719069345 | 9.536678617258826 | 1.045118181818172e-2 | 5.573809090909099e-2 | 4.180363636363538e-2 | 8.170777685950412e-2 | 7.790738016528927e-2 | 6.587239669421473e-2 | 1.0922720139669217e-4 | 3.1067347781900912e-3 | 1.747544013223058e-3 | 9.461612793580014e-3 | 9.708744318026302e-3 | 7.735971351465028e-3 | 9.72708219024596e-2 | 9.853296056663628e-2 | 8.795437084912283e-2 |
| user_003 | Sitting | 1.446047529883e12 | 0.460442 | -0.650847 | 9.46452 | 0.47786072727272727 | -0.6926505454545453 | 9.454069090909089 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.492320128700781 | 1.7418727272727252e-2 | 4.180354545454534e-2 | 1.0450909090911509e-2 | 5.130488429752066e-2 | 9.279201652892563e-2 | 6.17553719008262e-2 | 3.034120598016522e-4 | 1.7475364125702387e-3 | 1.0922150082649681e-4 | 3.8283135643719006e-3 | 1.240843196444929e-2 | 5.813010988429641e-3 | 6.1873367165299e-2 | 0.11139314146054635 | 7.624310453037468e-2 |
| user_003 | Sitting | 1.446047529891e12 | 0.575403 | -0.765807 | 9.50284 | 0.49876263636363627 | -0.678715818181818 | 9.44710181818182 | 0.331088612409 | 0.586460361249 | 90.30396806560002 | 9.550995604608874 | 9.485168028171538 | 7.664036363636373e-2 | 8.709118181818198e-2 | 5.573818181818169e-2 | 5.130490082644628e-2 | 8.139087603305785e-2 | 5.8588429752066e-2 | 5.873745338314064e-3 | 7.584873950487632e-3 | 3.1067449123966797e-3 | 3.828316097934638e-3 | 8.9927083140571e-3 | 5.34964704552959e-3 | 6.187338763907014e-2 | 9.482989145863818e-2 | 7.314128140475522e-2 |
| user_003 | Sitting | 1.44604753002e12 | 0.537083 | -0.689167 | 9.50284 | 0.4778606363636364 | -0.6403956363636364 | 9.502839999999999 | 0.28845814888899995 | 0.47495115388899994 | 90.30396806560002 | 9.542922894395511 | 9.537381176494081 | 5.922236363636357e-2 | 4.8771363636363585e-2 | 1.7763568394002505e-15 | 8.519144628099172e-2 | 8.424148760330576e-2 | 4.687074380165254e-2 | 3.5072883546776786e-3 | 2.3786459109504084e-3 | 3.1554436208840472e-30 | 1.0087168569314048e-2 | 1.018977686158527e-2 | 2.9942136691209083e-3 | 0.10043489716883294 | 0.10094442461862502 | 5.471940852312741e-2 |
| user_003 | Sitting | 1.446047530142e12 | 0.575403 | -0.765807 | 9.46452 | 0.3629000909090909 | -0.6891668181818181 | 9.447101818181817 | 0.331088612409 | 0.586460361249 | 89.5771388304 | 9.512869588302891 | 9.479941573052752 | 0.21250290909090908 | 7.664018181818189e-2 | 1.7418181818182887e-2 | 0.10039261983471072 | 6.618949586776866e-2 | 7.47398347107437e-2 | 4.515748637209917e-2 | 5.873717469123977e-3 | 3.0339305785127694e-4 | 1.3279947553097667e-2 | 6.071256061773864e-3 | 7.888219504132201e-3 | 0.11523865476955927 | 7.791826526414627e-2 | 8.881564898221597e-2 |
| user_003 | Sitting | 1.446047530158e12 | 0.498763 | -0.535886 | 9.50284 | 0.3698673636363637 | -0.6647811818181817 | 9.461036363636364 | 0.24876453016900002 | 0.287173804996 | 90.30396806560002 | 9.530997135702277 | 9.492622848471019 | 0.12889563636363632 | 0.12889518181818171 | 4.180363636363715e-2 | 9.849253719008265e-2 | 8.202431404958684e-2 | 7.125619834710778e-2 | 1.6614085073586766e-2 | 1.6613967895942123e-2 | 1.7475440132232066e-3 | 1.29092732124846e-2 | 8.401342212178822e-3 | 7.35314447483098e-3 | 0.11361898262387583 | 9.165883597438286e-2 | 8.575047798602047e-2 |
| user_003 | Sitting | 1.446047530198e12 | 0.422122 | -0.535886 | 9.57948 | 0.43257318181818183 | -0.6403957272727271 | 9.44361818181818 | 0.178186982884 | 0.287173804996 | 91.7664370704 | 9.603738743753913 | 9.47623788966848 | 1.0451181818181832e-2 | 0.10450972727272712 | 0.13586181818181942 | 9.057513223140498e-2 | 7.094005785123968e-2 | 5.7955041322314375e-2 | 1.0922720139669449e-4 | 1.0922283094619801e-2 | 1.8458433639669758e-2 | 1.2908162652194589e-2 | 7.0321907231382375e-3 | 4.926000012021046e-3 | 0.11361409530597244 | 8.385815835765914e-2 | 7.018546866710407e-2 |
| user_003 | Sitting | 1.446047530247e12 | 0.307161 | -0.880768 | 9.38788 | 0.46740963636363625 | -0.6264609999999999 | 9.457552727272729 | 9.434787992100001e-2 | 0.775752269824 | 88.13229089439999 | 9.43410785629171 | 9.490591922281 | 0.16024863636363623 | 0.25430700000000006 | 6.967272727272977e-2 | 7.505712396694214e-2 | 6.048910743801654e-2 | 5.067107438016582e-2 | 2.5679625456404915e-2 | 6.467205024900004e-2 | 4.854288925620183e-3 | 9.283960639918853e-3 | 9.24644060953268e-3 | 4.152623526371195e-3 | 9.635331151506342e-2 | 9.615841413798731e-2 | 6.444085293019634e-2 |
| user_003 | Sitting | 1.446047530328e12 | 0.345482 | -0.574206 | 9.4262 | 0.39773645454545453 | -0.6473628181818181 | 9.429683636363636 | 0.119357812324 | 0.329712530436 | 88.85324643999999 | 9.449990305961165 | 9.461319720505479 | 5.225445454545452e-2 | 7.315681818181807e-2 | 3.4836363636365775e-3 | 8.392447107438017e-2 | 8.550806611570248e-2 | 5.320462809917343e-2 | 2.7305280198429726e-3 | 5.351920046487588e-3 | 1.2135722314051077e-5 | 9.723052447205857e-3 | 1.2296954448760333e-2 | 5.915613004357611e-3 | 9.86055396375166e-2 | 0.1108916338086888 | 7.691302233274681e-2 |
| user_003 | Sitting | 1.446047530441e12 | 0.498763 | -0.612526 | 9.46452 | 0.5057300909090908 | -0.657813909090909 | 9.429683636363636 | 0.24876453016900002 | 0.375188100676 | 89.5771388304 | 9.49742551754132 | 9.466509525966101 | 6.967090909090812e-3 | 4.528790909090896e-2 | 3.4836363636364e-2 | 6.492278512396693e-2 | 5.3521900826446235e-2 | 4.402049586776839e-2 | 4.8540355735535836e-5 | 2.0509947098264346e-3 | 1.213572231404984e-3 | 5.985194005758828e-3 | 4.486975113920357e-3 | 4.422919159729566e-3 | 7.736403560931157e-2 | 6.698488720540147e-2 | 6.650503108584768e-2 |
| user_003 | Sitting | 1.446047530514e12 | 0.498763 | -0.880768 | 9.31124 | 0.4499914545454546 | -0.7031015454545453 | 9.440134545454544 | 0.24876453016900002 | 0.775752269824 | 86.6991903376 | 9.366093483282825 | 9.477717120133109 | 4.877154545454543e-2 | 0.1776664545454547 | 0.12889454545454448 | 6.2389223140495845e-2 | 6.713966942148762e-2 | 6.650578512396682e-2 | 2.378663646024791e-3 | 3.156536907075212e-2 | 1.6613803847933633e-2 | 6.276472416003001e-3 | 7.296975301380921e-3 | 6.55549654455295e-3 | 7.922419085104626e-2 | 8.542233490944227e-2 | 8.096602092577447e-2 |
| user_003 | Sitting | 1.446047530659e12 | 0.383802 | -0.727487 | 9.54116 | 0.4465077272727273 | -0.6821995454545454 | 9.461036363636365 | 0.147303975204 | 0.529237335169 | 91.0337341456 | 9.576548201516713 | 9.496631596516915 | 6.270572727272733e-2 | 4.5287454545454575e-2 | 8.012363636363418e-2 | 5.9222214876033095e-2 | 6.08056611570248e-2 | 6.428892561983478e-2 | 3.93200823280166e-3 | 2.050953539206614e-3 | 6.419797104131881e-3 | 4.411940753462063e-3 | 4.693273525742299e-3 | 5.314343126070618e-3 | 6.642244164032261e-2 | 6.850747058345023e-2 | 7.289954132963128e-2 |
| user_003 | Sitting | 1.446047531194e12 | 0.575403 | -0.612526 | 9.46452 | 0.4360567272727273 | -0.675232090909091 | 9.485421818181818 | 0.331088612409 | 0.375188100676 | 89.5771388304 | 9.501758550051933 | 9.52033802670179 | 0.13934627272727268 | 6.270609090909096e-2 | 2.090181818181769e-2 | 0.10197616528925618 | 5.8272214876033075e-2 | 4.9404297520661444e-2 | 1.9417383722983458e-2 | 3.93205383709918e-3 | 4.368860033057645e-4 | 1.2546293734994739e-2 | 5.233893928461308e-3 | 3.038343568444816e-3 | 0.11201023942030808 | 7.23456559059444e-2 | 5.512117168969484e-2 |
| user_003 | Sitting | 1.446047531283e12 | 0.307161 | -0.689167 | 9.50284 | 0.4325730909090908 | -0.6508465454545456 | 9.44710181818182 | 9.434787992100001e-2 | 0.47495115388899994 | 90.30396806560002 | 9.532747090918232 | 9.480348281329318 | 0.12541209090909078 | 3.832045454545441e-2 | 5.573818181818169e-2 | 8.709162809917352e-2 | 7.284034710743799e-2 | 5.162115702479333e-2 | 1.572819254619005e-2 | 1.4684572365702374e-3 | 3.1067449123966797e-3 | 1.0321058878268211e-2 | 7.570614707637863e-3 | 3.4983977688955513e-3 | 0.1015926123213111 | 8.700927943408027e-2 | 5.9147254956553576e-2 |
| user_003 | Sitting | 1.446047531348e12 | 0.613724 | -0.765807 | 9.57948 | 0.46044236363636365 | -0.7379379999999999 | 9.443618181818183 | 0.37665714817600005 | 0.586460361249 | 91.7664370704 | 9.629618610299424 | 9.484515872090418 | 0.1532816363636364 | 2.7869000000000144e-2 | 0.13586181818181764 | 8.424131404958675e-2 | 6.713955371900827e-2 | 6.777256198347087e-2 | 2.349526004631406e-2 | 7.76681161000008e-4 | 1.8458433639669276e-2 | 1.073036766763711e-2 | 7.2020997030293e-3 | 5.847211660405668e-3 | 0.10358748798786999 | 8.48651854592288e-2 | 7.646706258517891e-2 |
| user_003 | Sitting | 1.446047531509e12 | 0.383802 | -0.650847 | 9.46452 | 0.4499912727272728 | -0.6543302727272727 | 9.440134545454542 | 0.147303975204 | 0.42360181740899994 | 89.5771388304 | 9.494632411158054 | 9.473935581651636 | 6.618927272727282e-2 | 3.4832727272727793e-3 | 2.438545454545782e-2 | 4.940451239669421e-2 | 4.307072727272726e-2 | 5.193785123966954e-2 | 4.381019824165301e-3 | 1.2133188892562345e-5 | 5.946503933885893e-4 | 4.212253419712996e-3 | 3.38923822532682e-3 | 4.649084893764115e-3 | 6.490187531738198e-2 | 5.821716435319415e-2 | 6.818419827030392e-2 |
| user_003 | Sitting | 1.446047531598e12 | 0.498763 | -0.689167 | 9.46452 | 0.4813443636363637 | -0.6856832727272727 | 9.468003636363637 | 0.24876453016900002 | 0.47495115388899994 | 89.5771388304 | 9.502676176449349 | 9.506151313791571 | 1.741863636363633e-2 | 3.4837272727272772e-3 | 3.4836363636365775e-3 | 8.740837190082638e-2 | 5.605531404958677e-2 | 7.062280991735535e-2 | 3.034088927685939e-4 | 1.2136355710743833e-5 | 1.2135722314051077e-5 | 1.465137964412546e-2 | 5.0474440098054055e-3 | 7.304601585574778e-3 | 0.12104288349227914 | 7.104536585735487e-2 | 8.54669619535805e-2 |
| user_003 | Sitting | 1.44604753193e12 | 0.460442 | -0.880768 | 9.46452 | 0.4534750909090908 | -0.7170360909090907 | 9.422716363636363 | 0.21200683536400003 | 0.775752269824 | 89.5771388304 | 9.516559143702517 | 9.461390118682749 | 6.96690909090919e-3 | 0.1637319090909093 | 4.180363636363715e-2 | 3.0719553719008252e-2 | 7.727383471074388e-2 | 5.320462809917359e-2 | 4.853782228099312e-5 | 2.6808138054553784e-2 | 1.7475440132232066e-3 | 1.446381519275732e-3 | 9.485840937113464e-3 | 5.361782767843718e-3 | 3.8031322870441045e-2 | 9.7395281903763e-2 | 7.322419523520704e-2 |
| user_003 | Sitting | 1.446047531955e12 | 0.422122 | -0.650847 | 9.65612 | 0.47786072727272727 | -0.6996178181818181 | 9.44710181818182 | 0.178186982884 | 0.42360181740899994 | 93.24065345439999 | 9.687230886826894 | 9.485730492495545 | 5.573872727272727e-2 | 4.8770818181818165e-2 | 0.20901818181818044 | 4.402082644628096e-2 | 8.455785123966947e-2 | 6.808925619834692e-2 | 3.106805717983471e-3 | 2.3785927061239654e-3 | 4.3688600330577934e-2 | 4.2089599727242605e-3 | 1.0109191196815939e-2 | 7.540696546957087e-3 | 6.487649784570881e-2 | 0.10054447372588877 | 8.68371841261397e-2 |
| user_003 | Sitting | 1.446047532084e12 | 0.383802 | -0.650847 | 9.46452 | 0.47786072727272716 | -0.6612976363636363 | 9.450585454545456 | 0.147303975204 | 0.42360181740899994 | 89.5771388304 | 9.494632411158054 | 9.486668111432918 | 9.405872727272718e-2 | 1.0450636363636301e-2 | 1.3934545454544534e-2 | 6.555622314049582e-2 | 7.949095041322311e-2 | 7.03061157024793e-2 | 8.847044176165272e-3 | 1.0921580040495736e-4 | 1.941715570247677e-4 | 5.857225243961679e-3 | 1.1110982175987227e-2 | 7.402790611570207e-3 | 7.65325110261102e-2 | 0.10540864374417891 | 8.603947124180975e-2 |
| user_003 | Sitting | 1.446047532165e12 | 0.422122 | -0.574206 | 9.54116 | 0.44650772727272725 | -0.6369118181818182 | 9.433167272727273 | 0.178186982884 | 0.329712530436 | 91.0337341456 | 9.567739213571825 | 9.465520089793488 | 2.4385727272727253e-2 | 6.27058181818182e-2 | 0.1079927272727268 | 5.953895867768593e-2 | 3.927050413223136e-2 | 6.808925619834726e-2 | 5.946636946198338e-4 | 3.932019633851242e-3 | 1.166242914380155e-2 | 4.64693759204658e-3 | 2.0719397710060082e-3 | 7.326666535236722e-3 | 6.816845012208052e-2 | 4.551856512463907e-2 | 8.559594929222247e-2 |
| user_003 | Sitting | 1.446047532214e12 | 0.537083 | -0.612526 | 9.54116 | 0.463926 | -0.6299445454545455 | 9.443618181818179 | 0.28845814888899995 | 0.375188100676 | 91.0337341456 | 9.575874915388411 | 9.47633465958667 | 7.315699999999997e-2 | 1.7418545454545464e-2 | 9.754181818182062e-2 | 4.655442975206612e-2 | 4.655447107438018e-2 | 4.9087603305785876e-2 | 5.351946648999996e-3 | 3.0340572575206643e-4 | 9.514406294215351e-3 | 3.2932427384973705e-3 | 2.640112742971452e-3 | 3.719047265514745e-3 | 5.7386781914456314e-2 | 5.1382027431500325e-2 | 6.09839918791378e-2 |
| user_003 | Sitting | 1.446047532391e12 | 0.460442 | -0.535886 | 9.4262 | 0.41863836363636364 | -0.6299446363636363 | 9.461036363636364 | 0.21200683536400003 | 0.287173804996 | 88.85324643999999 | 9.452641275345213 | 9.492556516525278 | 4.1803636363636376e-2 | 9.405863636363632e-2 | 3.4836363636364e-2 | 9.659232231404959e-2 | 8.835825619834711e-2 | 6.49223140495872e-2 | 1.7475440132231415e-3 | 8.847027074586768e-3 | 1.213572231404984e-3 | 1.4473703594323817e-2 | 1.2876186440492116e-2 | 5.98070460586029e-3 | 0.12030670635639484 | 0.11347328514012502 | 7.733501539315997e-2 |
| user_003 | Sitting | 1.446047532529e12 | 0.498763 | -0.689167 | 9.34956 | 0.42560581818181814 | -0.6752322727272727 | 9.481938181818181 | 0.24876453016900002 | 0.47495115388899994 | 87.41427219360001 | 9.388183417342145 | 9.515991487267254 | 7.315718181818187e-2 | 1.3934727272727265e-2 | 0.13237818181818106 | 7.98074214876033e-2 | 4.908786776859502e-2 | 6.808925619834709e-2 | 5.3519732515785205e-3 | 1.9417662416528904e-4 | 1.7523983021487402e-2 | 7.866250311642372e-3 | 3.222624247057847e-3 | 6.397732154470296e-3 | 8.869188413627467e-2 | 5.676816226599067e-2 | 7.998582470957148e-2 |
| user_003 | Sitting | 1.446047532691e12 | 0.422122 | -0.842448 | 9.46452 | 0.42908936363636363 | -0.6787158181818183 | 9.405298181818182 | 0.178186982884 | 0.7097186327039999 | 89.5771388304 | 9.511311394649425 | 9.440375723637255 | 6.967363636363633e-3 | 0.16373218181818172 | 5.9221818181818264e-2 | 4.497070247933884e-2 | 8.96250743801653e-2 | 6.048859504132167e-2 | 4.854415604132226e-5 | 2.6808227362942118e-2 | 3.5072237487603405e-3 | 3.0802885080879025e-3 | 1.2626851415314798e-2 | 4.959097436513814e-3 | 5.550034691862658e-2 | 0.11236926365921776 | 7.042085938494229e-2 |
| user_003 | Sitting | 1.446047532788e12 | 0.460442 | -0.689167 | 9.54116 | 0.4360566363636364 | -0.6403957272727273 | 9.436650909090908 | 0.21200683536400003 | 0.47495115388899994 | 91.0337341456 | 9.57709205003549 | 9.468743163819505 | 2.438536363636362e-2 | 4.877127272727266e-2 | 0.104509090909092 | 4.117052066115701e-2 | 5.162148760330579e-2 | 5.35213223140498e-2 | 5.946459596776853e-4 | 2.3786370434380104e-3 | 1.0922150082644855e-2 | 2.744925226993988e-3 | 4.317064291385426e-3 | 4.449397099323852e-3 | 5.239203400321454e-2 | 6.570437041312721e-2 | 6.670380123594045e-2 |
| user_003 | Sitting | 1.446047532804e12 | 0.230521 | -0.574206 | 9.54116 | 0.41167100000000006 | -0.636912 | 9.454069090909092 | 5.3139931441e-2 | 0.329712530436 | 91.0337341456 | 9.561202152840247 | 9.484988102207778 | 0.18115000000000006 | 6.270600000000004e-2 | 8.709090909090733e-2 | 5.225482644628099e-2 | 6.0805702479338856e-2 | 6.017190082644642e-2 | 3.281532250000002e-2 | 3.932042436000005e-3 | 7.584826446280685e-3 | 5.409289030824944e-3 | 5.143409093702481e-3 | 5.145546261157036e-3 | 7.354786897541589e-2 | 7.171756475022337e-2 | 7.173246309138587e-2 |
| user_003 | Sitting | 1.446047532819e12 | 0.575403 | -0.727487 | 9.34956 | 0.42212199999999994 | -0.6403956363636364 | 9.450585454545454 | 0.331088612409 | 0.529237335169 | 87.41427219360001 | 9.395456249761265 | 9.482358118978716 | 0.15328100000000006 | 8.70913636363636e-2 | 0.10102545454545364 | 6.55560991735537e-2 | 7.157335537190083e-2 | 6.935603305785162e-2 | 2.349506496100002e-2 | 7.584905620041317e-3 | 1.0206142466115519e-2 | 7.620226583552217e-3 | 6.374646010217882e-3 | 6.172669667918895e-3 | 8.729390920076965e-2 | 7.984138031257904e-2 | 7.856633928037436e-2 |
| user_003 | Sitting | 1.446047532991e12 | 0.498763 | -0.689167 | 9.46452 | 0.41863836363636364 | -0.6578139090909092 | 9.408781818181819 | 0.24876453016900002 | 0.47495115388899994 | 89.5771388304 | 9.502676176449349 | 9.441844129998412 | 8.012463636363637e-2 | 3.1353090909090775e-2 | 5.573818181818169e-2 | 7.474038842975207e-2 | 6.58730082644628e-2 | 6.080528925619788e-2 | 6.41995735240496e-3 | 9.830163095537105e-4 | 3.1067449123966797e-3 | 6.935123140278735e-3 | 7.1513644837716e-3 | 6.11640404628094e-3 | 8.327738672820333e-2 | 8.456574060322301e-2 | 7.820744239700554e-2 |
| user_003 | Sitting | 1.446047533055e12 | 0.460442 | -0.650847 | 9.38788 | 0.42212200000000005 | -0.6578139090909091 | 9.415749090909088 | 0.21200683536400003 | 0.42360181740899994 | 88.13229089439999 | 9.421671802136444 | 9.44895072125354 | 3.8319999999999965e-2 | 6.966909090909135e-3 | 2.7869090909089067e-2 | 7.695723966942146e-2 | 6.523954545454545e-2 | 5.225454545454495e-2 | 1.4684223999999974e-3 | 4.8537822280992347e-5 | 7.766862280990708e-4 | 7.050963895679938e-3 | 7.138123901706236e-3 | 4.8355337184071805e-3 | 8.397001783779695e-2 | 8.448741860008646e-2 | 6.953800197307354e-2 |
| user_003 | Sitting | 1.446047533249e12 | 0.345482 | -0.650847 | 9.46452 | 0.4256057272727272 | -0.6578139090909091 | 9.436650909090908 | 0.119357812324 | 0.42360181740899994 | 89.5771388304 | 9.493160614891808 | 9.46968381931937 | 8.012372727272721e-2 | 6.966909090909135e-3 | 2.786909090909262e-2 | 5.85887603305785e-2 | 5.953899173553718e-2 | 4.4337190082644765e-2 | 6.419811672074369e-3 | 4.8537822280992347e-5 | 7.766862280992689e-4 | 4.047867299084896e-3 | 6.319509547628849e-3 | 3.5458374106687065e-3 | 6.362285201941906e-2 | 7.949534293044372e-2 | 5.954693451949233e-2 |
| user_003 | Sitting | 1.446047533443e12 | 0.498763 | -0.650847 | 9.4262 | 0.4499913636363637 | -0.657814 | 9.4262 | 0.24876453016900002 | 0.42360181740899994 | 88.85324643999999 | 9.461797545264748 | 9.460293159957134 | 4.8771636363636295e-2 | 6.9670000000000565e-3 | 0.0 | 5.193814049586775e-2 | 5.257162809917355e-2 | 2.660231404958728e-2 | 2.37867251358677e-3 | 4.853908900000079e-5 | 0.0 | 3.3914277338775323e-3 | 5.8075949420383155e-3 | 9.09075926070645e-4 | 5.823596598217919e-2 | 7.620757798302158e-2 | 3.0150885991470384e-2 |
| user_003 | Sitting | 1.446047533702e12 | 0.422122 | -0.650847 | 9.50284 | 0.4395403636363637 | -0.6612977272727272 | 9.443618181818183 | 0.178186982884 | 0.42360181740899994 | 90.30396806560002 | 9.534451052152557 | 9.477111134477436 | 1.7418363636363676e-2 | 1.0450727272727223e-2 | 5.9221818181818264e-2 | 3.4203123966942146e-2 | 2.786920661157025e-2 | 2.72357024793389e-2 | 3.0339939176859645e-4 | 1.0921770052892458e-4 | 3.5072237487603405e-3 | 1.6593011434440253e-3 | 1.3106807544943642e-3 | 1.1385514025544887e-3 | 4.073452029230276e-2 | 3.62033251856009e-2 | 3.374242733643341e-2 |
| user_003 | Sitting | 1.44604753435e12 | 0.498763 | -0.689167 | 9.50284 | 0.45695872727272735 | -0.6612978181818181 | 9.468003636363635 | 0.24876453016900002 | 0.47495115388899994 | 90.30396806560002 | 9.540842926579288 | 9.502149947082033 | 4.180427272727266e-2 | 2.7869181818181876e-2 | 3.4836363636365775e-2 | 2.945285950413223e-2 | 1.8051628099173548e-2 | 3.293619834710784e-2 | 1.7475972182561929e-3 | 7.766912952148793e-4 | 1.2135722314051078e-3 | 1.126443607117956e-3 | 5.284638936957168e-4 | 1.4783516273478882e-3 | 3.356253278758854e-2 | 2.2988342560865863e-2 | 3.844933845136855e-2 |
| user_003 | Sitting | 1.446047534415e12 | 0.498763 | -0.650847 | 9.46452 | 0.4639260909090909 | -0.6612978181818181 | 9.46452 | 0.24876453016900002 | 0.42360181740899994 | 89.5771388304 | 9.499973956699987 | 9.499015665677254 | 3.4836909090909085e-2 | 1.0450818181818144e-2 | 0.0 | 3.103636363636363e-2 | 1.805163636363636e-2 | 2.7552396694215275e-2 | 1.213610235008264e-3 | 1.0921960066942071e-4 | 0.0 | 1.2091900474124713e-3 | 5.284640664357619e-4 | 1.1595131047333121e-3 | 3.477341006304201e-2 | 2.2988346317988205e-2 | 3.405162411300395e-2 |
| user_003 | Sitting | 1.446047534803e12 | 0.422122 | -0.612526 | 9.46452 | 0.4604424545454545 | -0.6543305454545453 | 9.471487272727273 | 0.178186982884 | 0.375188100676 | 89.5771388304 | 9.493709175762653 | 9.505321529005101 | 3.832045454545452e-2 | 4.180454545454526e-2 | 6.967272727273155e-3 | 3.4520173553719e-2 | 1.3301223140495816e-2 | 2.026842975206639e-2 | 1.4684572365702459e-3 | 1.7476200206611408e-3 | 4.854288925620431e-5 | 1.6096746418955666e-3 | 3.4863351750112544e-4 | 9.443798455296936e-4 | 4.012075076435592e-2 | 1.8671730436709003e-2 | 3.0730763829259006e-2 |
| user_003 | Sitting | 1.446047534998e12 | 0.460442 | -0.689167 | 9.4262 | 0.453475 | -0.6543305454545454 | 9.461036363636362 | 0.21200683536400003 | 0.47495115388899994 | 88.85324643999999 | 9.462568595748884 | 9.494558667728176 | 6.967000000000001e-3 | 3.483645454545459e-2 | 3.483636363636222e-2 | 2.8502826446280997e-2 | 1.3934595041322238e-2 | 1.8051570247933538e-2 | 4.8539089000000014e-5 | 1.2135785652975235e-3 | 1.2135722314048601e-3 | 1.319511052964688e-3 | 3.7069852473929204e-4 | 5.924438984222251e-4 | 3.632507471382114e-2 | 1.9253532785940664e-2 | 2.434017046822444e-2 |
| user_003 | Sitting | 1.446047535515e12 | 0.460442 | -0.650847 | 9.46452 | 0.44650754545454546 | -0.6647814545454546 | 9.440134545454544 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.474119764777338 | 1.3934454545454555e-2 | 1.3934454545454611e-2 | 2.4385454545456042e-2 | 2.4068826446280996e-2 | 2.0585239669421454e-2 | 3.008595041322305e-2 | 1.941690234793391e-4 | 1.9416902347934065e-4 | 5.946503933885027e-4 | 8.980566373538687e-4 | 6.454093360931615e-4 | 1.2367404285499523e-3 | 2.9967593119132354e-2 | 2.5404907716682648e-2 | 3.516732046303716e-2 |
| user_003 | Sitting | 1.446047537963e12 | 2.91294 | -4.06135 | -17.7429 | 0.6694618181818182 | -0.9748269999999999 | 7.00156909090909 | 8.485219443599998 | 16.4945638225 | 314.81050041 | 18.433401305133568 | 10.321177531628067 | 2.2434781818181815 | 3.086523 | 24.74446909090909 | 0.2169370991735537 | 0.3173297520661157 | 2.264065123966943 | 5.033194352294213 | 9.526624229529 | 612.2887505909553 | 0.457926093891347 | 0.8678727182561082 | 55.66323812816227 | 0.6767023672866432 | 0.9315968646663149 | 7.4607799946226985 |
| user_003 | Sitting | 1.446047537996e12 | 0.498763 | -0.574206 | 9.38788 | 0.7008150000000001 | -0.9574087272727273 | 7.0050527272727265 | 0.24876453016900002 | 0.329712530436 | 88.13229089439999 | 9.418639389795374 | 10.325252885713102 | 0.20205200000000006 | 0.3832027272727273 | 2.3828272727272726 | 0.2704586033057851 | 0.4246898595041323 | 3.15778347107438 | 4.082501070400003e-2 | 0.14684433018925622 | 5.677865811652892 | 0.467783707752465 | 0.9122205865213097 | 57.8899981788081 | 0.6839471527482697 | 0.9551023958305778 | 7.608547704970253 |
| user_003 | Sitting | 1.446047538044e12 | 0.422122 | -0.919089 | 9.50284 | 0.6903640909090908 | -0.9643761818181816 | 7.005052727272727 | 0.178186982884 | 0.8447245899210001 | 90.30396806560002 | 9.556509804233187 | 10.325897387815681 | 0.2682420909090908 | 4.528718181818159e-2 | 2.4977872727272734 | 0.41043860330578513 | 0.5539019090909091 | 4.500261157024793 | 7.195381933528093e-2 | 2.050928837033037e-3 | 6.2389412597983505 | 0.5123842726084574 | 0.959901352937687 | 61.24369845327421 | 0.7158102210840925 | 0.9797455552018018 | 7.825835319841213 |
| user_003 | Sitting | 1.446047538101e12 | 0.383802 | -0.804128 | 9.54116 | 0.443024 | -0.7135526363636363 | 9.461036363636364 | 0.147303975204 | 0.646621840384 | 91.0337341456 | 9.582674989854764 | 9.498997704798395 | 5.9222e-2 | 9.057536363636365e-2 | 8.012363636363595e-2 | 0.13871318181818182 | 0.11464396694214875 | 0.9611742148760336 | 3.5072452839999997e-3 | 8.203896497859506e-3 | 6.419797104132165e-3 | 3.661955596183396e-2 | 2.796989594571301e-2 | 2.23701703117055 | 0.1913623681966597 | 0.1672420280483139 | 1.4956660827773525 |
| user_003 | Sitting | 1.446047538125e12 | 0.498763 | -0.804128 | 9.57948 | 0.47089336363636375 | -0.7240035454545454 | 9.450585454545454 | 0.24876453016900002 | 0.646621840384 | 91.7664370704 | 9.626101154722663 | 9.490513908927614 | 2.7869636363636263e-2 | 8.012445454545458e-2 | 0.12889454545454626 | 6.175596694214875e-2 | 5.922220661157021e-2 | 0.3128957024793395 | 7.767166310413167e-4 | 6.419928216206618e-3 | 1.661380384793409e-2 | 8.979472165661901e-3 | 4.764998892272725e-3 | 0.5763698394401208 | 9.476007685550862e-2 | 6.902897139804942e-2 | 0.7591902524664821 |
| user_003 | Sitting | 1.446047538262e12 | 0.537083 | -0.612526 | 9.50284 | 0.5092136363636363 | -0.6543302727272726 | 9.485421818181818 | 0.28845814888899995 | 0.375188100676 | 90.30396806560002 | 9.537694391998782 | 9.522259443822756 | 2.786936363636372e-2 | 4.180427272727261e-2 | 1.7418181818182887e-2 | 7.220681818181819e-2 | 6.175582644628099e-2 | 6.1121983471074254e-2 | 7.767014294958724e-4 | 1.7475972182561883e-3 | 3.0339305785127694e-4 | 9.30272696033734e-3 | 5.69174341896544e-3 | 5.808597998497331e-3 | 9.645064520436004e-2 | 7.544364399315187e-2 | 7.621415877970006e-2 |
| user_003 | Sitting | 1.446047538343e12 | 0.537083 | -0.574206 | 9.38788 | 0.4743770909090909 | -0.7065851818181819 | 9.471487272727272 | 0.28845814888899995 | 0.329712530436 | 88.13229089439999 | 9.420746338466236 | 9.51013996484748 | 6.270590909090906e-2 | 0.13237918181818187 | 8.360727272727253e-2 | 4.782130578512396e-2 | 6.175572727272729e-2 | 5.5104793388430065e-2 | 3.932031034917352e-3 | 1.7524247778851254e-2 | 6.990176052892529e-3 | 3.4631438297821194e-3 | 5.9587168723117985e-3 | 3.907702585123994e-3 | 5.88484819666754e-2 | 7.719272551420761e-2 | 6.251161960087095e-2 |
| user_003 | Sitting | 1.446047538481e12 | 0.422122 | -0.650847 | 9.50284 | 0.42908945454545455 | -0.7031014545454545 | 9.415749090909092 | 0.178186982884 | 0.42360181740899994 | 90.30396806560002 | 9.534451052152557 | 9.452520129048189 | 6.9674545454545544e-3 | 5.225445454545452e-2 | 8.709090909090911e-2 | 5.162155371900827e-2 | 7.600711570247934e-2 | 8.075702479338903e-2 | 4.854542284297533e-5 | 2.7305280198429726e-3 | 7.584826446280995e-3 | 5.528469067821186e-3 | 9.055587604848238e-3 | 7.911387701277323e-3 | 7.435367555017833e-2 | 9.516085121964935e-2 | 8.894598192879385e-2 |
| user_003 | Sitting | 1.446047538554e12 | 0.422122 | -0.535886 | 9.4262 | 0.4465077272727273 | -0.6647812727272726 | 9.422716363636363 | 0.178186982884 | 0.287173804996 | 88.85324643999999 | 9.450852195854086 | 9.457397142342744 | 2.438572727272731e-2 | 0.12889527272727264 | 3.4836363636365775e-3 | 4.940454545454545e-2 | 7.474023140495865e-2 | 5.637157024793347e-2 | 5.946636946198365e-4 | 1.6613991331437993e-2 | 1.2135722314051077e-5 | 4.071027434838466e-3 | 8.259009006755818e-3 | 4.830017480991683e-3 | 6.380460355521744e-2 | 9.087909004141612e-2 | 6.94983271812472e-2 |
| user_003 | Sitting | 1.446047538586e12 | 0.345482 | -0.689167 | 9.4262 | 0.4465078181818182 | -0.6299447272727272 | 9.398330909090907 | 0.119357812324 | 0.47495115388899994 | 88.85324643999999 | 9.457671775136468 | 9.430760240156756 | 0.10102581818181816 | 5.922227272727276e-2 | 2.786909090909262e-2 | 5.3204925619834725e-2 | 9.469222314049584e-2 | 4.5603966942148494e-2 | 1.020621593930578e-2 | 3.5072775869834753e-3 | 7.766862280992689e-4 | 4.033517999304282e-3 | 1.2079629896032291e-2 | 3.5149464811419576e-3 | 6.350998346169114e-2 | 0.10990736961656526 | 5.928698407864881e-2 |
| user_003 | Sitting | 1.446047538716e12 | 0.422122 | -0.650847 | 9.4262 | 0.46044236363636365 | -0.6508465454545453 | 9.457552727272729 | 0.178186982884 | 0.42360181740899994 | 88.85324643999999 | 9.458067204259704 | 9.49159945306404 | 3.832036363636365e-2 | 4.5454545460899e-7 | 3.13527272727292e-2 | 5.098814049586778e-2 | 5.57385867768595e-2 | 6.143867768595047e-2 | 1.4684502692231417e-3 | 2.0661157030569337e-13 | 9.829935074381372e-4 | 4.204520725820437e-3 | 5.136800779629599e-3 | 5.889135064763309e-3 | 6.484227576065199e-2 | 7.167147814597938e-2 | 7.674070018421325e-2 |
| user_003 | Sitting | 1.446047538942e12 | 0.307161 | -0.574206 | 9.57948 | 0.41863845454545456 | -0.6334282727272728 | 9.478454545454547 | 9.434787992100001e-2 | 0.329712530436 | 91.7664370704 | 9.601588279069095 | 9.509319384860925 | 0.11147745454545455 | 5.922227272727276e-2 | 0.10102545454545364 | 5.573864462809917e-2 | 5.478864462809916e-2 | 4.40204958677692e-2 | 1.2427222871933884e-2 | 3.5072775869834753e-3 | 1.0206142466115519e-2 | 4.095318860127722e-3 | 5.583616413305784e-3 | 2.7879063897821754e-3 | 6.39946783735001e-2 | 7.472360010937498e-2 | 5.280062868737621e-2 |
| user_003 | Sitting | 1.446047539177e12 | 0.345482 | -0.689167 | 9.46452 | 0.3907692727272727 | -0.6821995454545454 | 9.468003636363637 | 0.119357812324 | 0.47495115388899994 | 89.5771388304 | 9.495864773500779 | 9.501078447172395 | 4.5287272727272676e-2 | 6.9674545454545544e-3 | 3.4836363636365775e-3 | 6.84062644628099e-2 | 5.985547107438018e-2 | 6.302214876033106e-2 | 2.0509370710743756e-3 | 4.854542284297533e-5 | 1.2135722314051077e-5 | 7.985403573391432e-3 | 5.155520228918111e-3 | 8.248981431104595e-3 | 8.936108534139137e-2 | 7.180195142834289e-2 | 9.082390341261817e-2 |
| user_003 | Sitting | 1.446047539242e12 | 0.307161 | -0.574206 | 9.46452 | 0.42560581818181814 | -0.6403957272727273 | 9.471487272727272 | 9.434787992100001e-2 | 0.329712530436 | 89.5771388304 | 9.48689618583217 | 9.503357170811485 | 0.11844481818181812 | 6.618972727272732e-2 | 6.967272727271379e-3 | 6.492270247933883e-2 | 6.048900000000001e-2 | 7.347305785123949e-2 | 1.4029174954123954e-2 | 4.381079996438023e-3 | 4.8542889256179554e-5 | 7.264995577235909e-3 | 6.130837874947407e-3 | 9.196671019083402e-3 | 8.52349434048965e-2 | 7.829966714455054e-2 | 9.589927538351581e-2 |
| user_003 | Sitting | 1.44604753925e12 | 0.575403 | -0.650847 | 9.54116 | 0.4290894545454545 | -0.6473630909090909 | 9.46452 | 0.331088612409 | 0.42360181740899994 | 91.0337341456 | 9.580627566888193 | 9.497061195942141 | 0.1463135454545455 | 3.48390909090901e-3 | 7.663999999999938e-2 | 6.618947107438017e-2 | 5.1938214876033055e-2 | 6.428892561983429e-2 | 2.1407653583479354e-2 | 1.2137622553718444e-5 | 5.8736895999999044e-3 | 7.6180346566506385e-3 | 5.266982370293012e-3 | 6.86109609737033e-3 | 8.728135343044721e-2 | 7.257397860316749e-2 | 8.283173363735864e-2 |
| user_003 | Sitting | 1.446047539363e12 | 0.460442 | -0.727487 | 9.34956 | 0.4569588181818181 | -0.6717486363636362 | 9.4262 | 0.21200683536400003 | 0.529237335169 | 87.41427219360001 | 9.389116910771374 | 9.462187082718389 | 3.483181818181913e-3 | 5.573836363636375e-2 | 7.663999999999938e-2 | 8.455800826446282e-2 | 7.157352066115702e-2 | 6.365553719008284e-2 | 1.2132555578513057e-5 | 3.106765180859517e-3 | 5.8736895999999044e-3 | 9.295014362248687e-3 | 9.007034869194588e-3 | 5.014259810668705e-3 | 9.641065481703091e-2 | 9.49053995787099e-2 | 7.081143841688788e-2 |
| user_003 | Sitting | 1.446047539436e12 | 0.460442 | -0.765807 | 9.54116 | 0.42560581818181814 | -0.7135525454545454 | 9.447101818181817 | 0.21200683536400003 | 0.586460361249 | 91.0337341456 | 9.582911944822044 | 9.484323546353263 | 3.483618181818188e-2 | 5.225445454545463e-2 | 9.405818181818226e-2 | 7.030644628099174e-2 | 6.903965289256202e-2 | 6.903933884297525e-2 | 1.2135595636694255e-3 | 2.7305280198429843e-3 | 8.846941566942232e-3 | 6.440833891956421e-3 | 8.33402396231856e-3 | 8.223606738993262e-3 | 8.025480603650115e-2 | 9.129087556989778e-2 | 9.068410411419006e-2 |
| user_003 | Sitting | 1.446047539468e12 | 0.460442 | -0.765807 | 9.34956 | 0.44999136363636355 | -0.7170361818181819 | 9.4262 | 0.21200683536400003 | 0.586460361249 | 87.41427219360001 | 9.392163722498294 | 9.46466076088866 | 1.0450636363636467e-2 | 4.8770818181818165e-2 | 7.663999999999938e-2 | 6.840630578512398e-2 | 3.7686694214876054e-2 | 7.727338842975164e-2 | 1.0921580040496085e-4 | 2.3785927061239654e-3 | 5.8736895999999044e-3 | 6.29520499386251e-3 | 2.36428181288505e-3 | 9.046629361382369e-3 | 7.934232788280483e-2 | 4.862388109648437e-2 | 9.511377061909788e-2 |
| user_003 | Sitting | 1.446047539525e12 | 0.460442 | -0.727487 | 9.38788 | 0.4290893636363636 | -0.6821993636363636 | 9.429683636363636 | 0.21200683536400003 | 0.529237335169 | 88.13229089439999 | 9.427276121178004 | 9.464803624816936 | 3.1352636363636444e-2 | 4.528763636363642e-2 | 4.180363636363715e-2 | 6.397267768595044e-2 | 5.510515702479342e-2 | 6.650578512396699e-2 | 9.829878069504182e-4 | 2.050970007404964e-3 | 1.7475440132232066e-3 | 8.124416326288506e-3 | 5.511913618561984e-3 | 5.330891838317037e-3 | 9.013554418922928e-2 | 7.424226302155655e-2 | 7.301295664686534e-2 |
| user_003 | Sitting | 1.446047539541e12 | 0.345482 | -0.727487 | 9.4262 | 0.41515472727272723 | -0.6891666363636363 | 9.4262 | 0.119357812324 | 0.529237335169 | 88.85324643999999 | 9.460541294634941 | 9.461245060511372 | 6.967272727272722e-2 | 3.832036363636371e-2 | 0.0 | 6.618949586776862e-2 | 6.143910743801655e-2 | 6.682247933884321e-2 | 4.854288925619827e-3 | 1.468450269223146e-3 | 0.0 | 8.582264722732536e-3 | 5.798763261621339e-3 | 6.938323421187104e-3 | 9.264051339847236e-2 | 7.614961104051247e-2 | 8.329659909736474e-2 |
| user_003 | Sitting | 1.446047539613e12 | 0.460442 | -0.574206 | 9.38788 | 0.4012200909090909 | -0.6856830909090909 | 9.44361818181818 | 0.21200683536400003 | 0.329712530436 | 88.13229089439999 | 9.41668786039975 | 9.477938578653424 | 5.922190909090913e-2 | 0.11147709090909086 | 5.573818181818169e-2 | 6.4289347107438e-2 | 8.930847107438017e-2 | 6.143867768595079e-2 | 3.5072345163719054e-3 | 1.2427141797553708e-2 | 3.1067449123966797e-3 | 5.837369956217128e-3 | 1.0567076387593537e-2 | 6.332640552967716e-3 | 7.640268291242873e-2 | 0.10279628586477985 | 7.957788984993078e-2 |
| user_003 | Sitting | 1.446047539662e12 | 0.460442 | -0.574206 | 9.50284 | 0.42212209090909086 | -0.664781090909091 | 9.468003636363637 | 0.21200683536400003 | 0.329712530436 | 90.30396806560002 | 9.531300406104092 | 9.501586987084501 | 3.8319909090909154e-2 | 9.0575090909091e-2 | 3.4836363636364e-2 | 6.618954545454546e-2 | 8.550803305785125e-2 | 3.9586776859504624e-2 | 1.468415432735542e-3 | 8.203847093190098e-3 | 1.213572231404984e-3 | 8.463145261121711e-3 | 8.296535774210368e-3 | 2.9379480474831074e-3 | 9.199535456272621e-2 | 9.108532139818341e-2 | 5.4202841691954745e-2 |
| user_003 | Sitting | 1.446047539743e12 | 0.307161 | -0.650847 | 9.38788 | 0.443024 | -0.6647812727272728 | 9.447101818181817 | 9.434787992100001e-2 | 0.42360181740899994 | 88.13229089439999 | 9.415425672359694 | 9.48130930593024 | 0.13586299999999996 | 1.3934272727272878e-2 | 5.9221818181818264e-2 | 6.36560826446281e-2 | 4.053709090909094e-2 | 4.718743801652924e-2 | 1.845875476899999e-2 | 1.9416395643802073e-4 | 3.5072237487603405e-3 | 5.967562775254698e-3 | 2.700786287416983e-3 | 2.8695467035311996e-3 | 7.725000178158378e-2 | 5.196908973050214e-2 | 5.3568150085019736e-2 |
| user_003 | Sitting | 1.44604753984e12 | 0.345482 | -0.650847 | 9.57948 | 0.4465079090909091 | -0.6647812727272727 | 9.44710181818182 | 0.119357812324 | 0.42360181740899994 | 91.7664370704 | 9.607777927290629 | 9.481999925423107 | 0.10102590909090908 | 1.3934272727272767e-2 | 0.13237818181818106 | 8.139092561983471e-2 | 7.917409917355371e-2 | 6.04885950413228e-2 | 1.0206234307644627e-2 | 1.9416395643801764e-4 | 1.7523983021487402e-2 | 7.2760015071224645e-3 | 1.0569256395184072e-2 | 5.581329016979755e-3 | 8.529948128284523e-2 | 0.10280688885081618 | 7.47082928260294e-2 |
| user_003 | Sitting | 1.446047539938e12 | 0.537083 | -0.574206 | 9.4262 | 0.4499914545454545 | -0.6787157272727273 | 9.457552727272727 | 0.28845814888899995 | 0.329712530436 | 88.85324643999999 | 9.458933191397696 | 9.493288075858345 | 8.70915454545455e-2 | 0.10450972727272734 | 3.135272727272742e-2 | 5.7638661157024804e-2 | 7.410695041322317e-2 | 3.895338842975268e-2 | 7.584937289661166e-3 | 1.0922283094619848e-2 | 9.829935074380258e-4 | 4.463789009020284e-3 | 8.106787283856505e-3 | 2.737157005559762e-3 | 6.68115933728592e-2 | 9.003769923680027e-2 | 5.2317845956802944e-2 |
| user_003 | Sitting | 1.446047540156e12 | 0.345482 | -0.804128 | 9.46452 | 0.453475 | -0.6926506363636363 | 9.443618181818183 | 0.119357812324 | 0.646621840384 | 89.5771388304 | 9.504899709260902 | 9.480333405363366 | 0.107993 | 0.11147736363636362 | 2.090181818181769e-2 | 4.718766942148758e-2 | 5.4788471074380146e-2 | 6.333884297520678e-2 | 1.1662488049e-2 | 1.2427202603314046e-2 | 4.368860033057645e-4 | 5.102591182545451e-3 | 4.019181194645379e-3 | 5.653040103380977e-3 | 7.143242388821376e-2 | 6.339701250568026e-2 | 7.518670163919267e-2 |
| user_003 | Sitting | 1.446047540172e12 | 0.498763 | -0.765807 | 9.4262 | 0.45347509090909094 | -0.6926505454545454 | 9.45406909090909 | 0.24876453016900002 | 0.586460361249 | 88.85324643999999 | 9.470399745069793 | 9.490850263665708 | 4.528790909090907e-2 | 7.315645454545461e-2 | 2.7869090909090843e-2 | 5.2888297520661126e-2 | 6.555606611570247e-2 | 6.3655537190083e-2 | 2.0509947098264446e-3 | 5.351866841661166e-3 | 7.766862280991699e-4 | 5.354138748736284e-3 | 5.332058540061606e-3 | 5.5967744817431225e-3 | 7.31719806260312e-2 | 7.30209458995267e-2 | 7.481159323088316e-2 |
| user_003 | Sitting | 1.446047540269e12 | 0.460442 | -0.650847 | 9.2346 | 0.45695872727272724 | -0.6682647272727272 | 9.394847272727274 | 0.21200683536400003 | 0.42360181740899994 | 85.27783716 | 9.26895063169359 | 9.43032809691739 | 3.4832727272727793e-3 | 1.741772727272728e-2 | 0.16024727272727368 | 5.383861983471073e-2 | 7.347342975206611e-2 | 8.645752066115685e-2 | 1.2133188892562345e-5 | 3.0337722334710765e-4 | 2.567918841652923e-2 | 5.776714665064613e-3 | 7.215338384983472e-3 | 1.0210555456048083e-2 | 7.600470159841832e-2 | 8.494314795781631e-2 | 0.10104729316536927 |
| user_003 | Sitting | 1.446047540318e12 | 0.422122 | -0.535886 | 9.65612 | 0.45695872727272724 | -0.668264909090909 | 9.440134545454546 | 0.178186982884 | 0.287173804996 | 93.24065345439999 | 9.680186684268026 | 9.475438618282498 | 3.483672727272724e-2 | 0.132378909090909 | 0.2159854545454536 | 6.428949586776858e-2 | 5.7955223140495844e-2 | 8.962446280991737e-2 | 1.213597567074378e-3 | 1.752417557209915e-2 | 4.66497165752062e-2 | 6.929614733910591e-3 | 4.520045244982718e-3 | 1.2364094543050317e-2 | 8.324430751655389e-2 | 6.723128174430945e-2 | 0.11119395011892651 |
| user_003 | Sitting | 1.446047540325e12 | 0.383802 | -0.574206 | 9.46452 | 0.4360567272727273 | -0.6508466363636364 | 9.44361818181818 | 0.147303975204 | 0.329712530436 | 89.5771388304 | 9.489686788089479 | 9.476578365490541 | 5.225472727272734e-2 | 7.664063636363638e-2 | 2.0901818181819465e-2 | 5.6688727272727266e-2 | 5.763855371900824e-2 | 8.930776859504132e-2 | 2.7305565223471146e-3 | 5.873787142223143e-3 | 4.368860033058388e-4 | 5.49977403832757e-3 | 4.470402678275729e-3 | 1.2349752325770074e-2 | 7.41604614220244e-2 | 6.686106997555251e-2 | 0.11112943950983499 |
| user_003 | Sitting | 1.44604754035e12 | 0.345482 | -0.804128 | 9.34956 | 0.44650772727272714 | -0.6647813636363636 | 9.408781818181819 | 0.119357812324 | 0.646621840384 | 87.41427219360001 | 9.390434060591023 | 9.44335301311665 | 0.10102572727272713 | 0.1393466363636363 | 5.9221818181818264e-2 | 5.478842148760328e-2 | 6.143899999999997e-2 | 8.645752066115717e-2 | 1.0206197570983441e-2 | 1.941748506585949e-2 | 3.5072237487603405e-3 | 4.3148482379737e-3 | 5.454524538504126e-3 | 1.1695526568294479e-2 | 6.568750442796331e-2 | 7.38547529851947e-2 | 0.10814585784159501 |
| user_003 | Sitting | 1.446047540366e12 | 0.460442 | -0.727487 | 9.38788 | 0.44999136363636355 | -0.668265 | 9.412265454545453 | 0.21200683536400003 | 0.529237335169 | 88.13229089439999 | 9.427276121178004 | 9.447275769909304 | 1.0450636363636467e-2 | 5.9222e-2 | 2.4385454545454266e-2 | 6.017223966942149e-2 | 6.365590909090904e-2 | 6.238876033057863e-2 | 1.0921580040496085e-4 | 3.5072452839999997e-3 | 5.946503933884161e-4 | 5.200762819015026e-3 | 5.541682701910585e-3 | 7.90476821637862e-3 | 7.21163145135345e-2 | 7.444247914941163e-2 | 8.890876343971171e-2 |
| user_003 | Sitting | 1.44604754048e12 | 0.498763 | -0.765807 | 9.4262 | 0.4987627272727273 | -0.7414219090909092 | 9.44361818181818 | 0.24876453016900002 | 0.586460361249 | 88.85324643999999 | 9.470399745069793 | 9.486452058500035 | 2.7272727270988284e-7 | 2.43850909090908e-2 | 1.741818181818111e-2 | 6.397279338842976e-2 | 7.505698347107435e-2 | 5.6371570247933954e-2 | 7.43801652797708e-14 | 5.946326586446228e-4 | 3.0339305785121503e-4 | 5.997347520679191e-3 | 8.279990516842221e-3 | 4.944755219233634e-3 | 7.744254335104957e-2 | 9.099445322019481e-2 | 7.031895348505718e-2 |
| user_003 | Sitting | 1.446047540593e12 | 0.460442 | -0.689167 | 9.50284 | 0.42560572727272733 | -0.668265 | 9.502839999999999 | 0.21200683536400003 | 0.47495115388899994 | 90.30396806560002 | 9.538916398357468 | 9.536132101776438 | 3.483627272727269e-2 | 2.0901999999999976e-2 | 1.7763568394002505e-15 | 5.858879338842975e-2 | 4.3703983471074376e-2 | 9.310809917355378e-2 | 1.213565897528923e-3 | 4.36893603999999e-4 | 3.1554436208840472e-30 | 4.6480467704342575e-3 | 2.341116897894815e-3 | 1.3706746729977431e-2 | 6.817658520661077e-2 | 4.838508962371378e-2 | 0.11707581616191036 |
| user_003 | Sitting | 1.446047540649e12 | 0.652044 | -0.574206 | 9.50284 | 0.4465077272727273 | -0.6299446363636364 | 9.443618181818183 | 0.42516137793599995 | 0.329712530436 | 90.30396806560002 | 9.542475673218771 | 9.476115480764772 | 0.20553627272727265 | 5.573863636363641e-2 | 5.9221818181818264e-2 | 8.360787603305782e-2 | 6.93565371900826e-2 | 7.442314049586765e-2 | 4.2245159406619805e-2 | 3.1067955836776907e-3 | 3.5072237487603405e-3 | 1.1187097730272723e-2 | 6.7685262636356025e-3 | 8.376958139143426e-3 | 0.10576907738215703 | 8.227105361933565e-2 | 9.152572392034616e-2 |
| user_003 | Sitting | 1.44604754073e12 | 0.422122 | -0.765807 | 9.38788 | 0.47437709090909086 | -0.6856831818181817 | 9.450585454545454 | 0.178186982884 | 0.586460361249 | 88.13229089439999 | 9.428517287385805 | 9.487907311509131 | 5.225509090909086e-2 | 8.012381818181835e-2 | 6.270545454545484e-2 | 7.125673553719011e-2 | 5.415497520661158e-2 | 7.378975206611554e-2 | 2.7305945259173503e-3 | 6.419826240033085e-3 | 3.931974029752103e-3 | 7.686441355646129e-3 | 3.738938656548464e-3 | 6.12853976859502e-3 | 8.767235228762901e-2 | 6.114686137937469e-2 | 7.828499069805794e-2 |
| user_003 | Sitting | 1.446047540852e12 | 0.537083 | -0.650847 | 9.31124 | 0.44650772727272725 | -0.6856831818181818 | 9.401814545454545 | 0.28845814888899995 | 0.42360181740899994 | 86.6991903376 | 9.349398392618532 | 9.437789541464076 | 9.057527272727273e-2 | 3.483618181818182e-2 | 9.057454545454569e-2 | 4.782117355371898e-2 | 5.732195867768597e-2 | 9.72251239669423e-2 | 8.203880029619835e-3 | 1.2135595636694218e-3 | 8.203748284297563e-3 | 3.1343671547543177e-3 | 4.990056541979716e-3 | 1.4301397123365868e-2 | 5.598541912636109e-2 | 7.064033226124942e-2 | 0.11958844895459539 |
| user_003 | Sitting | 1.446047540925e12 | 0.460442 | -0.574206 | 9.46452 | 0.4848280000000001 | -0.6891670000000001 | 9.454069090909089 | 0.21200683536400003 | 0.329712530436 | 89.5771388304 | 9.49309529058884 | 9.492107685859132 | 2.4386000000000074e-2 | 0.11496100000000009 | 1.0450909090911509e-2 | 4.433761157024795e-2 | 7.062329752066118e-2 | 5.478809917355385e-2 | 5.946769960000036e-4 | 1.321603152100002e-2 | 1.0922150082649681e-4 | 2.8111200184079647e-3 | 6.5169880832118745e-3 | 3.972794186626621e-3 | 5.301999640143296e-2 | 8.072786435433477e-2 | 6.303010539913939e-2 |
| user_003 | Sitting | 1.446047540933e12 | 0.345482 | -0.765807 | 9.4262 | 0.4813443636363636 | -0.699617909090909 | 9.450585454545454 | 0.119357812324 | 0.586460361249 | 88.85324643999999 | 9.463565111181566 | 9.48928338586127 | 0.13586236363636361 | 6.618909090909098e-2 | 2.4385454545454266e-2 | 5.098821487603307e-2 | 7.34735619834711e-2 | 5.1304462809917434e-2 | 1.8458581852859497e-2 | 4.3809957553719095e-3 | 5.946503933884161e-4 | 4.1317176202314045e-3 | 6.804936827912101e-3 | 3.6694011287753763e-3 | 6.427843822178168e-2 | 8.2492040997372e-2 | 6.057558195160304e-2 |
| user_003 | Sitting | 1.446047541118e12 | 0.383802 | -0.612526 | 9.38788 | 0.443024 | -0.6717485454545453 | 9.46452 | 0.147303975204 | 0.375188100676 | 88.13229089439999 | 9.415666889301043 | 9.499323719754216 | 5.9222e-2 | 5.9222545454545306e-2 | 7.664000000000115e-2 | 5.76385950413223e-2 | 7.030660330578511e-2 | 7.632330578512493e-2 | 3.5072452839999997e-3 | 3.507309890115685e-3 | 5.873689600000177e-3 | 5.4037578223200605e-3 | 7.833167167294515e-3 | 9.64789923966959e-3 | 7.351025657906562e-2 | 8.850518158443897e-2 | 9.822372035139776e-2 |
| user_003 | Sitting | 1.446047541158e12 | 0.537083 | -0.574206 | 9.50284 | 0.4674095454545455 | -0.6299444545454546 | 9.474970909090908 | 0.28845814888899995 | 0.329712530436 | 90.30396806560002 | 9.535310102189914 | 9.508476174425821 | 6.967345454545448e-2 | 5.573845454545456e-2 | 2.786909090909262e-2 | 5.9855570247933865e-2 | 0.10007608264462808 | 9.089123966942222e-2 | 4.854390268297512e-3 | 3.1067753151157044e-3 | 7.766862280992689e-4 | 6.554502585894815e-3 | 1.4869794603729529e-2 | 1.105123003816697e-2 | 8.095988257090554e-2 | 0.12194176726507423 | 0.10512483074025361 |
| user_003 | Sitting | 1.446047541175e12 | 0.460442 | -0.689167 | 9.46452 | 0.4778605454545454 | -0.6194935454545454 | 9.450585454545454 | 0.21200683536400003 | 0.47495115388899994 | 89.5771388304 | 9.500741908906535 | 9.483763584885539 | 1.741854545454541e-2 | 6.96734545454546e-2 | 1.393454545454631e-2 | 6.523947933884294e-2 | 8.677486776859503e-2 | 6.04885950413228e-2 | 3.0340572575206454e-4 | 4.8543902682975275e-3 | 1.9417155702481723e-4 | 6.91085958427573e-3 | 1.1449687157067612e-2 | 5.543818602554532e-3 | 8.313157994574463e-2 | 0.10700321096615564 | 7.445682374742112e-2 |
| user_003 | Sitting | 1.446047541192e12 | 0.498763 | -0.689167 | 9.34956 | 0.4848280000000001 | -0.6229772727272729 | 9.443618181818183 | 0.24876453016900002 | 0.47495115388899994 | 87.41427219360001 | 9.388183417342145 | 9.477402355192464 | 1.393499999999992e-2 | 6.61897272727271e-2 | 9.405818181818226e-2 | 6.492290082644625e-2 | 8.772497520661153e-2 | 5.352132231405013e-2 | 1.9418422499999777e-4 | 4.381079996437993e-3 | 8.846941566942232e-3 | 6.8987275471728005e-3 | 1.1558912227920352e-2 | 3.8139265490609105e-3 | 8.305857901007457e-2 | 0.10751238174238516 | 6.1756995952368915e-2 |
| user_003 | Sitting | 1.4460475412e12 | 0.498763 | -0.689167 | 9.27292 | 0.4778607272727273 | -0.6369119090909092 | 9.422716363636365 | 0.24876453016900002 | 0.47495115388899994 | 85.98704532639998 | 9.311861307518384 | 9.457088391091498 | 2.0902272727272686e-2 | 5.225509090909075e-2 | 0.14979636363636573 | 5.5422041322314014e-2 | 7.91742066115702e-2 | 6.397223140495938e-2 | 4.3690500516528754e-4 | 2.730594525917339e-3 | 2.243895055867831e-2 | 5.508618789685197e-3 | 9.86099776814199e-3 | 5.74350639699485e-3 | 7.422006999245688e-2 | 9.930255670496098e-2 | 7.578592479474569e-2 |
| user_003 | Sitting | 1.44604754196e12 | 0.460442 | -0.650847 | 9.46452 | 0.44999127272727274 | -0.678716 | 9.44361818181818 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.478809400678257 | 1.0450727272727278e-2 | 2.7869000000000033e-2 | 2.0901818181819465e-2 | 4.528754545454547e-2 | 4.148710743801653e-2 | 1.9951735537190984e-2 | 1.0921770052892573e-4 | 7.766811610000018e-4 | 4.368860033058388e-4 | 2.986535792406464e-3 | 2.2738162678332076e-3 | 5.769984336589671e-4 | 5.4649206695124714e-2 | 4.7684549571461905e-2 | 2.40207916950913e-2 |
| user_003 | Sitting | 1.446047542413e12 | 0.422122 | -0.765807 | 9.4262 | 0.4534750000000001 | -0.6821996363636363 | 9.450585454545454 | 0.178186982884 | 0.586460361249 | 88.85324643999999 | 9.46667279376091 | 9.486175161191744 | 3.1353000000000075e-2 | 8.360736363636367e-2 | 2.4385454545454266e-2 | 2.7235900826446276e-2 | 2.8819264462809923e-2 | 2.4702148760330802e-2 | 9.830106090000048e-4 | 6.990191254223147e-3 | 5.946503933884161e-4 | 1.3062715649120968e-3 | 1.4066530360563494e-3 | 1.1032474830954288e-3 | 3.6142379071003296e-2 | 3.750537342910146e-2 | 3.3215169472628446e-2 |
| user_003 | Standing | 1.446047630907e12 | -0.689167 | -9.54116 | 0.344284 | -0.8041277272727272 | -9.4262 | 0.4035064545454546 | 0.47495115388899994 | 91.0337341456 | 0.11853147265599999 | 9.572210652307282 | 9.470404311628682 | 0.11496072727272721 | 0.11495999999999995 | 5.9222454545454606e-2 | 8.497683663911854e-2 | 6.639318273645532e-2 | 8.869623911845728e-2 | 1.3215968815074367e-2 | 1.3215801599999988e-2 | 3.507299122388437e-3 | 1.0229017505059551e-2 | 6.166949670227525e-3 | 1.3160927524382622e-2 | 0.10113860541385546 | 7.852992850007903e-2 | 0.11472108578802165 |
| user_003 | Standing | 1.446047631231e12 | -0.727487 | -9.50284 | 0.344284 | -0.8006440909090909 | -9.429683636363636 | 0.396539 | 0.529237335169 | 90.30396806560002 | 0.11853147265599999 | 9.536862003480234 | 9.472512914924552 | 7.315709090909095e-2 | 7.315636363636457e-2 | 5.2254999999999996e-2 | 6.460626446280994e-2 | 6.77725619834712e-2 | 4.845475206611569e-2 | 5.351959950280998e-3 | 5.351853540496005e-3 | 2.7305850249999997e-3 | 5.803188286028555e-3 | 5.375021737640885e-3 | 3.176314455302029e-3 | 7.617866030607623e-2 | 7.331453974240638e-2 | 5.635880104563997e-2 |
| user_003 | Standing | 1.446047631943e12 | -0.842448 | -9.38788 | 0.382604 | -0.7971605454545454 | -9.40529818181818 | 0.3860879090909091 | 0.7097186327039999 | 88.13229089439999 | 0.146385820816 | 9.433366066676305 | 9.44757028741933 | 4.5287454545454575e-2 | 1.741818181818111e-2 | 3.483909090909121e-3 | 6.428952066115706e-2 | 6.365553719008267e-2 | 5.130495867768594e-2 | 2.050953539206614e-3 | 3.0339305785121503e-4 | 1.2137622553719216e-5 | 6.695712793529687e-3 | 5.6033939666416425e-3 | 3.987201700823439e-3 | 8.182733524641803e-2 | 7.485582119409045e-2 | 6.314429270190172e-2 |
| user_003 | Standing | 1.446047632979e12 | -0.689167 | -9.38788 | 0.305964 | -0.7379380909090908 | -9.412265454545452 | 0.3617023636363636 | 0.47495115388899994 | 88.13229089439999 | 9.361396929600001e-2 | 9.418113187766698 | 9.448590535195043 | 4.877109090909082e-2 | 2.438545454545249e-2 | 5.5738363636363586e-2 | 5.415501652892563e-2 | 3.0085950413224018e-2 | 6.270585950413224e-2 | 2.378619308462801e-3 | 5.946503933883295e-4 | 3.1067651808594984e-3 | 4.3258806551938435e-3 | 1.1749585694966677e-3 | 4.971305307842974e-3 | 6.577142734648415e-2 | 3.427766867067636e-2 | 7.05074840555453e-2 |
| user_003 | Standing | 1.446047633237e12 | -0.689167 | -9.38788 | 0.382604 | -0.7483889999999999 | -9.422716363636363 | 0.36170236363636366 | 0.47495115388899994 | 88.13229089439999 | 0.146385820816 | 9.420914386040506 | 9.459752343029551 | 5.9221999999999886e-2 | 3.4836363636364e-2 | 2.0901636363636344e-2 | 4.750432231404958e-2 | 3.008595041322434e-2 | 5.732199173553716e-2 | 3.5072452839999863e-3 | 1.213572231404984e-3 | 4.3687840267768517e-4 | 3.7378388638985736e-3 | 1.0999377406462195e-3 | 4.3214836728707705e-3 | 6.1137867675431515e-2 | 3.31653092951991e-2 | 6.573799261363836e-2 |
| user_003 | Standing | 1.446047633367e12 | -0.804128 | -9.34956 | 0.305964 | -0.7449053636363636 | -9.422716363636363 | 0.36866963636363637 | 0.646621840384 | 87.41427219360001 | 9.361396929600001e-2 | 9.38906321223156 | 9.459703198070578 | 5.922263636363634e-2 | 7.31563636363628e-2 | 6.270563636363635e-2 | 4.4337380165289236e-2 | 3.6736528925621285e-2 | 5.352162809917354e-2 | 3.5073206578595012e-3 | 5.351853540495745e-3 | 3.931996831768593e-3 | 3.2524059982727287e-3 | 1.6526647296770385e-3 | 4.056701973697217e-3 | 5.702986935170664e-2 | 4.065297934564008e-2 | 6.369224421934916e-2 |
| user_003 | Standing | 1.446047634532e12 | -0.765807 | -9.46452 | 0.305964 | -0.7344543636363635 | -9.429683636363636 | 0.3477676363636364 | 0.586460361249 | 89.5771388304 | 9.361396929600001e-2 | 9.500379632464432 | 9.465045583166999 | 3.13526363636365e-2 | 3.4836363636364e-2 | 4.1803636363636376e-2 | 5.225490909090909e-2 | 3.1669421487604606e-2 | 5.447188429752066e-2 | 9.829878069504217e-4 | 1.213572231404984e-3 | 1.7475440132231415e-3 | 4.147163891128476e-3 | 1.3569944042074078e-3 | 3.7643580316784374e-3 | 6.43984773975944e-2 | 3.683740496027656e-2 | 6.1354364406115705e-2 |
| user_003 | Standing | 1.446047634662e12 | -0.535886 | -9.46452 | 0.420925 | -0.7065850909090909 | -9.4262 | 0.36170236363636366 | 0.287173804996 | 89.5771388304 | 0.177177855625 | 9.48901946941943 | 9.460098310075162 | 0.17069909090909097 | 3.8320000000000576e-2 | 5.922263636363634e-2 | 6.365589256198347e-2 | 3.451966942148859e-2 | 5.035481818181819e-2 | 2.9138179637190103e-2 | 1.4684224000000442e-3 | 3.5073206578595012e-3 | 6.600807368122467e-3 | 1.6449419972952627e-3 | 3.3550440600578517e-3 | 8.124535290170427e-2 | 4.055788452687421e-2 | 5.792274216624979e-2 |
| user_003 | Standing | 1.446047635634e12 | -0.765807 | -9.38788 | 0.420925 | -0.6543303636363635 | -9.419232727272727 | 0.35125145454545453 | 0.586460361249 | 88.13229089439999 | 0.177177855625 | 9.4284637726023 | 9.449174059864886 | 0.11147663636363647 | 3.135272727272742e-2 | 6.967354545454546e-2 | 6.302253719008266e-2 | 5.035438016528928e-2 | 8.265804958677685e-2 | 1.2427040454950437e-2 | 9.829935074380258e-4 | 4.854402936206612e-3 | 5.892509816134489e-3 | 4.032369550713747e-3 | 8.761074538072124e-3 | 7.676268505031913e-2 | 6.350094133722545e-2 | 9.36006118466761e-2 |
| user_003 | Standing | 1.446047636993e12 | -0.535886 | -9.38788 | 0.382604 | -0.6612977272727272 | -9.419232727272727 | 0.3512512727272728 | 0.287173804996 | 88.13229089439999 | 0.146385820816 | 9.410943125968405 | 9.449580293155417 | 0.1254117272727272 | 3.135272727272742e-2 | 3.13527272727272e-2 | 4.053711570247934e-2 | 2.818578512396722e-2 | 6.808980165289256e-2 | 1.572810133752891e-2 | 9.829935074380258e-4 | 9.82993507438012e-4 | 3.375969831589033e-3 | 1.227914448685197e-3 | 7.375343415595795e-3 | 5.810309657487312e-2 | 3.504161024675089e-2 | 8.587981960621363e-2 |
| user_003 | Standing | 1.44604763764e12 | -0.574206 | -9.38788 | 0.344284 | -0.647362909090909 | -9.412265454545452 | 0.3721534545454545 | 0.329712530436 | 88.13229089439999 | 0.11853147265599999 | 9.411723269279223 | 9.442694565988013 | 7.3156909090909e-2 | 2.438545454545249e-2 | 2.786945454545453e-2 | 6.713971074380164e-2 | 3.07193388429758e-2 | 7.790748760330576e-2 | 5.351933347735523e-3 | 5.946503933883295e-4 | 7.767064966611562e-4 | 5.882607076129975e-3 | 1.161719599699493e-3 | 8.702586125975955e-3 | 7.669815562404336e-2 | 3.408400797587474e-2 | 9.328765259119749e-2 |
| user_003 | Standing | 1.446047637899e12 | -0.689167 | -9.34956 | 0.267643 | -0.6299445454545455 | -9.422716363636363 | 0.33383318181818183 | 0.47495115388899994 | 87.41427219360001 | 7.163277544900001e-2 | 9.378744911923876 | 9.45028455359085 | 5.9222454545454495e-2 | 7.31563636363628e-2 | 6.619018181818181e-2 | 5.858897520661155e-2 | 4.3070413223140876e-2 | 6.713981818181818e-2 | 3.507299122388424e-3 | 5.351853540495745e-3 | 4.381140169123966e-3 | 5.521843364183319e-3 | 2.8949213956423483e-3 | 6.59534203249737e-3 | 7.430910687246428e-2 | 5.380447375118865e-2 | 8.121171117823692e-2 |
| user_003 | Standing | 1.446047638223e12 | -0.535886 | -9.46452 | 0.382604 | -0.6125263636363636 | -9.422716363636363 | 0.35821881818181817 | 0.287173804996 | 89.5771388304 | 0.146385820816 | 9.487396821900726 | 9.449822535242 | 7.664036363636362e-2 | 4.180363636363715e-2 | 2.4385181818181834e-2 | 5.3838578512396665e-2 | 5.0037685950412907e-2 | 4.465428925619834e-2 | 5.873745338314047e-3 | 1.7475440132232066e-3 | 5.946370923057859e-4 | 5.01765396693313e-3 | 3.3957957529676466e-3 | 2.419472113918106e-3 | 7.083540052073631e-2 | 5.827345667598282e-2 | 4.918812980707953e-2 |
| user_003 | Standing | 1.446047638418e12 | -0.650847 | -9.46452 | 0.420925 | -0.6299447272727271 | -9.419232727272725 | 0.33731672727272727 | 0.42360181740899994 | 89.5771388304 | 0.177177855625 | 9.496205479212946 | 9.446599682163269 | 2.0902272727272853e-2 | 4.528727272727551e-2 | 8.360827272727273e-2 | 3.357000826446282e-2 | 4.2437024793388935e-2 | 5.2888462809917346e-2 | 4.369050051652945e-4 | 2.050937071074632e-3 | 6.990343268438016e-3 | 1.615187078950413e-3 | 2.38963404838471e-3 | 3.374911324686701e-3 | 4.018939012911757e-2 | 4.888388331939997e-2 | 5.8093986992516715e-2 |
| user_003 | Standing | 1.446047638676e12 | -0.535886 | -9.38788 | 0.344284 | -0.5916243636363636 | -9.422716363636363 | 0.33731672727272727 | 0.287173804996 | 88.13229089439999 | 0.11853147265599999 | 9.409463118162055 | 9.447580888994437 | 5.573836363636364e-2 | 3.4836363636364e-2 | 6.967272727272711e-3 | 4.1170694214876034e-2 | 3.1352727272728226e-2 | 4.497091735537189e-2 | 3.1067651808595045e-3 | 1.213572231404984e-3 | 4.854288925619812e-5 | 2.55075781586326e-3 | 1.4309119855748112e-3 | 2.652259924018782e-3 | 5.0505027629566344e-2 | 3.7827397287876034e-2 | 5.150009634960678e-2 |
| user_003 | Standing | 1.44604763913e12 | -0.535886 | -9.46452 | 0.344284 | -0.5707222727272726 | -9.429683636363633 | 0.3129310909090909 | 0.287173804996 | 89.5771388304 | 0.11853147265599999 | 9.485928742513934 | 9.452492415666207 | 3.483627272727263e-2 | 3.483636363636755e-2 | 3.13529090909091e-2 | 4.465428099173552e-2 | 2.4385454545455557e-2 | 5.447174380165289e-2 | 1.213565897528919e-3 | 1.2135722314052313e-3 | 9.830049084628104e-4 | 2.854158935062359e-3 | 8.417778296018401e-4 | 4.706525393682193e-3 | 5.3424329055799656e-2 | 2.9013407755757337e-2 | 6.860412082143603e-2 |
| user_003 | Standing | 1.446047639194e12 | -0.650847 | -9.31124 | 0.114362 | -0.5742059999999999 | -9.419232727272725 | 0.29551272727272726 | 0.42360181740899994 | 86.6991903376 | 1.3078667044000002e-2 | 9.334659652180845 | 9.441912906219029 | 7.664100000000007e-2 | 0.10799272727272502 | 0.18115072727272724 | 5.0038123966942155e-2 | 3.420297520661206e-2 | 6.808974380165288e-2 | 5.873842881000011e-3 | 1.1662429143801166e-2 | 3.2815585991438e-2 | 3.360561736989483e-3 | 1.9019986608564915e-3 | 7.600397437682192e-3 | 5.797035222412818e-2 | 4.361190962175918e-2 | 8.718025830245166e-2 |
| user_003 | Standing | 1.446047640165e12 | -0.727487 | -9.2346 | 0.382604 | -0.6438792727272727 | -9.408781818181815 | 0.26067590909090915 | 0.529237335169 | 85.27783716 | 0.146385820816 | 9.27110890433205 | 9.435124200958072 | 8.360772727272725e-2 | 0.17418181818181466 | 0.12192809090909085 | 4.9404785123966934e-2 | 5.5738181818181846e-2 | 7.347362809917354e-2 | 6.9902520597107404e-3 | 3.033930578512274e-2 | 1.4866459352735522e-2 | 3.4002625236025552e-3 | 5.125687806461234e-3 | 9.953659155622087e-3 | 5.831177002632106e-2 | 7.15939090039176e-2 | 9.976802672009749e-2 |
| user_003 | Standing | 1.446047640294e12 | -0.842448 | -9.46452 | 0.382604 | -0.6508465454545456 | -9.41574909090909 | 0.28157790909090913 | 0.7097186327039999 | 89.5771388304 | 0.146385820816 | 9.509639492847244 | 9.443550494292248 | 0.1916014545454544 | 4.877090909091031e-2 | 0.10102609090909087 | 7.695737190082642e-2 | 6.460561983471083e-2 | 8.519137190082643e-2 | 3.671111738393383e-2 | 2.378601573553838e-3 | 1.0206271044371893e-2 | 9.261906457942896e-3 | 5.9509169238166106e-3 | 1.1252200041873776e-2 | 9.62387991297839e-2 | 7.714218640806475e-2 | 0.10607638776784292 |
| user_003 | Standing | 1.446047640683e12 | -0.650847 | -9.50284 | 0.382604 | -0.647363 | -9.426200000000001 | 0.28506163636363635 | 0.42360181740899994 | 90.30396806560002 | 0.146385820816 | 9.53278320868701 | 9.453927396237697 | 3.4839999999999316e-3 | 7.663999999999938e-2 | 9.754236363636365e-2 | 7.252361983471071e-2 | 6.36555371900822e-2 | 0.10514338016528924 | 1.2138255999999524e-5 | 5.8736895999999044e-3 | 9.514512703768598e-3 | 8.447700141810665e-3 | 5.77770706897057e-3 | 1.3642986799874529e-2 | 9.19113711235485e-2 | 7.601122988723817e-2 | 0.1168031968735211 |
| user_003 | Standing | 1.446047641654e12 | -0.842448 | -9.57948 | 0.267643 | -0.6508466363636364 | -9.44361818181818 | 0.2571923636363636 | 0.7097186327039999 | 91.7664370704 | 7.163277544900001e-2 | 9.620176114736829 | 9.470404615826363 | 0.1916013636363636 | 0.13586181818181942 | 1.0450636363636412e-2 | 8.170772727272727e-2 | 8.297388429752059e-2 | 4.592101652892564e-2 | 3.671108254731403e-2 | 1.8458433639669758e-2 | 1.0921580040495968e-4 | 1.1734313433839967e-2 | 1.353243362764841e-2 | 3.3395934129819694e-3 | 0.10832503604356643 | 0.11632898876741089 | 5.7789215369149716e-2 |
| user_003 | Standing | 1.446047642107e12 | -0.535886 | -9.46452 | 0.114362 | -0.6403955454545454 | -9.433167272727273 | 0.23977390909090912 | 0.287173804996 | 89.5771388304 | 1.3078667044000002e-2 | 9.480368732409094 | 9.460083485751973 | 0.10450954545454538 | 3.135272727272742e-2 | 0.1254119090909091 | 0.1121106033057851 | 0.1073593388429755 | 8.265771900826446e-2 | 1.0922245091115687e-2 | 9.829935074380258e-4 | 1.572814694182645e-2 | 1.9075439881976707e-2 | 1.8643779216829522e-2 | 1.8134347264641623e-2 | 0.1381138656398289 | 0.13654222503251337 | 0.13466383057317813 |
| user_003 | Standing | 1.446047642172e12 | -0.574206 | -9.54116 | 0.382604 | -0.636911909090909 | -9.443618181818179 | 0.25370854545454546 | 0.329712530436 | 91.0337341456 | 0.146385820816 | 9.566077173891708 | 9.470739264005575 | 6.2705909090909e-2 | 9.754181818182062e-2 | 0.12889545454545454 | 0.11401075206611568 | 0.11622677685950465 | 9.247529752066116e-2 | 3.932031034917345e-3 | 9.514406294215351e-3 | 1.6614038202479336e-2 | 1.9274024774400447e-2 | 1.9508725243576372e-2 | 1.960499642810594e-2 | 0.13883092153551546 | 0.13967363832726765 | 0.14001784324901573 |
| user_003 | Standing | 1.446047642949e12 | -0.535886 | -9.50284 | 0.229323 | -0.5672386363636363 | -9.440134545454546 | 0.250225 | 0.287173804996 | 90.30396806560002 | 5.2589038329e-2 | 9.52070012703504 | 9.460813853842183 | 3.135263636363628e-2 | 6.270545454545484e-2 | 2.0901999999999976e-2 | 6.048906611570246e-2 | 3.071933884297483e-2 | 4.560432231404959e-2 | 9.829878069504078e-4 | 3.931974029752103e-3 | 4.36893603999999e-4 | 4.64585674370473e-3 | 1.2985222876033022e-3 | 2.936898062541696e-3 | 6.816052188550738e-2 | 3.603501474404169e-2 | 5.4193155126285975e-2 |
| user_003 | Standing | 1.446047643791e12 | -0.420925 | -9.50284 | 0.382604 | -0.5358857272727273 | -9.422716363636361 | 0.2328068181818182 | 0.177177855625 | 90.30396806560002 | 0.146385820816 | 9.519849355007725 | 9.441925086561081 | 0.11496072727272733 | 8.01236363636395e-2 | 0.1497971818181818 | 7.63238595041322e-2 | 4.655404958677745e-2 | 5.890551239669421e-2 | 1.3215968815074391e-2 | 6.419797104132735e-3 | 2.2439195680669415e-2 | 1.429944640338467e-2 | 3.63961344673185e-3 | 5.249324134230651e-3 | 0.11958029270487955 | 6.032920890192287e-2 | 7.245221966393198e-2 |
| user_003 | Standing | 1.446047644761e12 | -0.497565 | -9.46452 | 0.267643 | -0.511499909090909 | -9.429683636363636 | 0.2502249090909091 | 0.24757092922499999 | 89.5771388304 | 7.163277544900001e-2 | 9.481368178436803 | 9.447264955731209 | 1.3934909090909053e-2 | 3.4836363636364e-2 | 1.741809090909091e-2 | 5.4471834710743815e-2 | 4.433719008264509e-2 | 3.483661157024793e-2 | 1.9418169137189978e-4 | 1.213572231404984e-3 | 3.0338989091735545e-4 | 4.018076853086403e-3 | 3.7731063921863624e-3 | 2.742723741573253e-3 | 6.338830217860708e-2 | 6.142561674241751e-2 | 5.237102005473307e-2 |
| user_003 | Standing | 1.446047646381e12 | -0.574206 | -9.46452 | 0.344284 | -0.4731796363636364 | -9.41574909090909 | 0.2711270909090909 | 0.329712530436 | 89.5771388304 | 0.11853147265599999 | 9.488170678981907 | 9.432077816933814 | 0.10102636363636358 | 4.877090909091031e-2 | 7.31569090909091e-2 | 6.682293388429751e-2 | 5.3204628099174235e-2 | 5.6688652892561975e-2 | 1.0206326149586765e-2 | 2.378601573553838e-3 | 5.35193334773554e-3 | 5.782216449574002e-3 | 3.949625989481666e-3 | 3.964020835802403e-3 | 7.604088669639512e-2 | 6.28460499115232e-2 | 6.296047042234042e-2 |
| user_003 | Standing | 1.446047646445e12 | -0.459245 | -9.38788 | 0.229323 | -0.48014700000000005 | -9.422716363636363 | 0.2746107272727273 | 0.210905970025 | 88.13229089439999 | 5.2589038329e-2 | 9.401903312774174 | 9.43942770260395 | 2.090200000000003e-2 | 3.4836363636364e-2 | 4.5287727272727285e-2 | 6.112236363636363e-2 | 4.687074380165367e-2 | 5.3521719008264475e-2 | 4.368936040000013e-4 | 1.213572231404984e-3 | 2.050978241528927e-3 | 5.186448298261457e-3 | 3.067028003005348e-3 | 3.5668501872141243e-3 | 7.20170000643005e-2 | 5.5380754807111e-2 | 5.972311267184694e-2 |
| user_003 | Standing | 1.44604764651e12 | -0.574206 | -9.54116 | 0.344284 | -0.4940816363636364 | -9.426199999999998 | 0.2885453636363636 | 0.329712530436 | 91.0337341456 | 0.11853147265599999 | 9.56462117120652 | 9.444022785372637 | 8.01243636363636e-2 | 0.11496000000000173 | 5.573863636363635e-2 | 6.523946280991734e-2 | 4.9720991735537984e-2 | 5.352174380165291e-2 | 6.419913648132226e-3 | 1.3215801600000398e-2 | 3.1067955836776846e-3 | 5.659752639225393e-3 | 3.632993961833309e-3 | 3.566852951106687e-3 | 7.52313275120504e-2 | 6.027432257465287e-2 | 5.9723135811063095e-2 |
| user_003 | Walking | 1.446047679584e12 | -2.56686 | -10.34589 | -0.268841 | -2.37526 | -10.115966666666667 | 0.944633 | 6.5887702596 | 107.03743989210001 | 7.2275483281e-2 | 10.662949199681156 | 10.471786160882395 | 0.19160000000000021 | 0.2299233333333337 | 1.213474 | 8.302666666666673e-2 | 0.22992277777777836 | 0.449198 | 3.671056000000008e-2 | 5.286473921111128e-2 | 1.4725191486759999 | 1.333817013333336e-2 | 8.810738774537086e-2 | 0.49683577435866666 | 0.11549099589722724 | 0.29682888630551246 | 0.7048657846417761 |
| user_003 | Walking | 1.446047679648e12 | -2.91174 | -9.2346 | -1.11189 | -2.50938 | -9.895624999999999 | 0.4305022499999999 | 8.4782298276 | 85.27783716 | 1.2362993721000002 | 9.746402739457261 | 10.290440305526111 | 0.40235999999999983 | 0.6610249999999986 | 1.54239225 | 0.16286 | 0.33769833333333343 | 0.7224965624999999 | 0.16189356959999987 | 0.4369540506249982 | 2.3789738528600624 | 5.0477019999999984e-2 | 0.1753190534652777 | 0.9673702939840156 | 0.22467091489554225 | 0.41871118144286246 | 0.9835498431620106 |
| user_003 | Walking | 1.446047679714e12 | -2.0687 | -9.57948 | -0.958607 | -2.421244 | -9.832396 | 0.15268039999999994 | 4.279519690000001 | 91.7664370704 | 0.918927380449 | 9.847074902774377 | 10.201767224975764 | 0.35254399999999997 | 0.25291599999999903 | 1.1112874 | 0.2007968 | 0.32074186666666654 | 0.80025473 | 0.12428727193599998 | 6.39665030559995e-2 | 1.2349596853987599 | 6.523907038719998e-2 | 0.15304854338342205 | 1.0208881722669645 | 0.25541940096085103 | 0.3912141911835792 | 1.0103901089514706 |
| user_003 | Walking | 1.446047680749e12 | -0.995729 | -8.39155 | 1.30229 | -2.7027253636363637 | -9.175375454545453 | -0.10162490909090907 | 0.991476241441 | 70.4181114025 | 1.6959592440999998 | 8.550178178730604 | 10.034046038882586 | 1.7069963636363639 | 0.7838254545454522 | 1.403914909090909 | 1.6648737190082643 | 0.7987106611570245 | 2.2143453057851237 | 2.9138365854677692 | 0.6143823431933847 | 1.9709770719677355 | 4.793715662380766 | 1.7562285270555222 | 6.099185223480105 | 2.18945556300665 | 1.3252277264891201 | 2.469652854852298 |
| user_003 | Walking | 1.446047680879e12 | -2.14534 | -9.96268 | 1.26397 | -1.8561953636363635 | -9.262466363636365 | 0.9121214545454546 | 4.6024837156 | 99.25499278240001 | 1.5976201609 | 10.269133199004676 | 9.595484485092896 | 0.2891446363636365 | 0.7002136363636353 | 0.35184854545454547 | 0.9500893388429752 | 0.8227794214876031 | 1.808972809917355 | 8.36046207378596e-2 | 0.4902991365495853 | 0.12379739893847935 | 1.1134641619282062 | 1.7791144310392937 | 4.574982385900888 | 1.0552081130886959 | 1.3338344841243586 | 2.1389208461046163 |
| user_003 | Walking | 1.446047681331e12 | -2.41358 | -7.24194 | -0.422122 | -2.5459590000000003 | -9.168406363636365 | 0.3164146363636364 | 5.8253684164 | 52.4456949636 | 0.178186982884 | 7.645210942994575 | 9.622119321250128 | 0.13237900000000025 | 1.926466363636365 | 0.7385366363636364 | 0.9576910165289256 | 1.0479508264462807 | 1.194580123966942 | 1.7524199641000066e-2 | 3.7112726502223192 | 0.5454363632513142 | 1.6246740838005926 | 1.4249213524337336 | 1.748376268873452 | 1.2746270371369786 | 1.1937006963362857 | 1.3222618004288909 |
| user_003 | Walking | 1.446047681915e12 | -1.72382 | -8.50651 | 0.152682 | -2.9430970909090903 | -8.931518181818182 | -0.7008159090909089 | 2.9715553923999996 | 72.36071238010001 | 2.3311793124000002e-2 | 8.680759158369964 | 9.863686110314818 | 1.2192770909090904 | 0.4250081818181819 | 0.8534979090909088 | 1.7291638099173554 | 1.0723361157024789 | 1.766850909090909 | 1.4866366244157343 | 0.18063195461239676 | 0.7284586808225533 | 5.3292291302925765 | 2.027118169973629 | 4.631297047916401 | 2.3085123197186053 | 1.4237690016198656 | 2.152044852673011 |
| user_003 | Walking | 1.44604768308e12 | -1.26397 | -7.97003 | 0.650847 | -2.6156328181818185 | -9.304270909090908 | -0.5266319090909091 | 1.5976201609 | 63.5213782009 | 0.42360181740899994 | 8.095838448191083 | 10.195105110337266 | 1.3516628181818184 | 1.3342409090909078 | 1.177478909090909 | 1.7788858099173555 | 0.9665576033057852 | 1.6474559008264462 | 1.8269923740552156 | 1.780198803491732 | 1.3864565813539171 | 6.173944335417497 | 1.5980932189392185 | 5.193812224823833 | 2.4847423076483195 | 1.2641571179798887 | 2.2789936868766953 |
| user_003 | Walking | 1.446047683597e12 | -3.71647 | -10.65245 | 0.727487 | -1.5635655454545452 | -9.558577272727275 | 0.9086370909090907 | 13.812149260900002 | 113.4746910025 | 0.529237335169 | 11.305577278430722 | 9.886379907429324 | 2.152904454545455 | 1.0938727272727249 | 0.18115009090909073 | 1.3937824462809922 | 0.8601490909090902 | 1.4929075371900828 | 4.634997590401663 | 1.1965575434710691 | 3.281535543637184e-2 | 2.867568569354223 | 1.4904467409425983 | 3.32652396830064 | 1.693389668491639 | 1.2208385400791533 | 1.8238760835924792 |
| user_003 | Walking | 1.446047683727e12 | -1.8771 | -9.34956 | -7.7239e-2 | -2.159272727272727 | -9.314720909090909 | 0.8180616363636362 | 3.52350441 | 87.41427219360001 | 5.965863121e-3 | 9.536442862342385 | 9.649491378034925 | 0.282172727272727 | 3.483909090909165e-2 | 0.8953006363636362 | 1.074868801652893 | 0.6308609917355367 | 1.1185721487603304 | 7.962144801652879e-2 | 1.2137622553719527e-3 | 0.801563229473132 | 1.6366407131942973 | 0.6092058806637107 | 1.4778986670610335 | 1.2793125940106653 | 0.780516419214683 | 1.2156885567697977 |
| user_003 | Walking | 1.446047684763e12 | -1.41725 | -8.54483 | 0.535886 | -2.7166602727272724 | -9.161439090909091 | -0.2618743636363637 | 2.0085975625 | 73.01411972889998 | 0.287173804996 | 8.678127165258411 | 9.973846093809122 | 1.2994102727272725 | 0.6166090909090922 | 0.7977603636363637 | 1.6227539752066116 | 0.7645065289256194 | 1.8409585537190083 | 1.6884670568691647 | 0.38020677099173716 | 0.6364215977892232 | 4.2806235768678 | 0.8269072979024034 | 4.606044262947701 | 2.0689667896966832 | 0.9093444330408601 | 2.1461696724508297 |
| user_003 | Walking | 1.446047685086e12 | -2.18366 | -9.57948 | 0.689167 | -2.6051827272727275 | -9.175373636363636 | 0.6926499090909091 | 4.768370995600001 | 91.7664370704 | 0.47495115388899994 | 9.849353238659328 | 9.64232140030103 | 0.4215227272727273 | 0.4041063636363642 | 3.482909090909092e-3 | 1.1331395454545456 | 0.5349014876033061 | 1.1983802809917354 | 0.17768140960743806 | 0.16330195313140541 | 1.2130655735537198e-5 | 1.9829541042932683 | 0.45450676466250944 | 1.7708770012784947 | 1.4081740319624092 | 0.6741711686675049 | 1.3307430260115942 |
| user_003 | Walking | 1.446047687546e12 | -1.8771 | -8.96635 | 1.30229 | -2.5424754545454546 | -9.11615 | 0.152682 | 3.52350441 | 80.3954323225 | 1.6959592440999998 | 9.252831781492626 | 9.79489048617874 | 0.6653754545454547 | 0.14979999999999905 | 1.149608 | 1.3972666942148761 | 0.45952801652892605 | 1.826388785123967 | 0.4427244955115704 | 2.2440039999999713e-2 | 1.3215985536639998 | 2.848263819498798 | 0.4674070052281754 | 4.43694763761664 | 1.687680010991064 | 0.683671708664455 | 2.1064063325048754 |
| user_003 | Walking | 1.446047687999e12 | -2.10702 | -8.50651 | -1.07357 | -1.922383636363636 | -9.408779090909091 | 0.4174404545454545 | 4.439533280399999 | 72.36071238010001 | 1.1525525448999998 | 8.829088186523 | 9.707851614316736 | 0.18463636363636393 | 0.9022690909090905 | 1.4910104545454543 | 1.0020287520661157 | 0.6609490909090908 | 1.251585561983471 | 3.409058677685961e-2 | 0.8140895124099166 | 2.2231121755638426 | 1.3171834867227503 | 0.8584210792066113 | 1.7665195376922902 | 1.1476861446940754 | 0.926510161415735 | 1.32910478807816 |
| user_003 | Walking | 1.446047688063e12 | -2.60518 | -8.27659 | -0.422122 | -2.033860909090909 | -9.349557272727274 | 0.28157754545454544 | 6.7869628323999995 | 68.5019420281 | 0.178186982884 | 8.687179740478724 | 9.670566566573536 | 0.5713190909090908 | 1.0729672727272739 | 0.7036995454545454 | 0.9193704876033059 | 0.7575411570247934 | 1.1682942396694216 | 0.32640550363719 | 1.151258768343804 | 0.4951930502729338 | 1.1475787221806768 | 0.9630710310996242 | 1.5729835182858294 | 1.0712510080185114 | 0.9813618247617054 | 1.254186396946574 |
| user_003 | Walking | 1.446047689099e12 | -4.78944 | -11.15061 | 1.11069 | -1.6820099090909089 | -9.286850909090909 | 1.4033162727272726 | 22.938735513599998 | 124.33610337210001 | 1.2336322760999998 | 12.18640517797599 | 9.603071921107578 | 3.107430090909091 | 1.8637590909090918 | 0.2926262727272726 | 1.4004335206611571 | 0.6150263636363636 | 1.4010657520661156 | 9.65612176988728 | 3.473597948946284 | 8.563013549025612e-2 | 2.525419334934868 | 0.6853445784876789 | 2.4557176957492683 | 1.5891567999838367 | 0.8278554091673732 | 1.5670729707800044 |
| user_003 | Walking | 1.446047689423e12 | -2.2603 | -7.39522 | -0.958607 | -2.3439056363636364 | -9.23807909090909 | 0.2850610909090909 | 5.1089560899999995 | 54.6892788484 | 0.918927380449 | 7.792121811089006 | 9.686183649343109 | 8.360563636363638e-2 | 1.8428590909090907 | 1.243668090909091 | 1.0967207190082644 | 0.9507247933884297 | 1.283254867768595 | 6.989902431768598e-3 | 3.39612962894628 | 1.5467103203454629 | 1.9360527094687148 | 1.2617876186290011 | 1.9974827090385192 | 1.3914211114787338 | 1.123293202431583 | 1.4133232853945763 |
| user_003 | Walking | 1.446047691882e12 | -4.09967 | -9.2346 | -2.18486 | -2.939613636363637 | -9.248530909090908 | 1.0514659999999998 | 16.8072941089 | 85.27783716 | 4.7736132196000005 | 10.33725033500205 | 9.866399368302162 | 1.1600563636363628 | 1.3930909090907662e-2 | 3.236326 | 0.8816842975206609 | 0.5000657851239663 | 1.2335327851239668 | 1.3457307668132212 | 1.9407022809913373e-4 | 10.473805978276 | 0.9549407656270471 | 0.49907817604537946 | 2.182993822858251 | 0.9772107068729072 | 0.7064546525045889 | 1.4774957945314942 |
| user_003 | Walking | 1.446047692724e12 | -2.37526 | -9.08132 | 0.650847 | -3.1904381818181817 | -8.760819090909091 | -0.9829931818181816 | 5.6418600676 | 82.47037294239999 | 0.42360181740899994 | 9.40934826794125 | 9.711024072087437 | 0.8151781818181818 | 0.32050090909090834 | 1.6338401818181816 | 1.1299748760330577 | 0.9947448760330573 | 1.8054882066115703 | 0.6645154681123967 | 0.10272083272809869 | 2.6694337397236687 | 3.8177683449762583 | 1.624831435103606 | 4.349292087728119 | 1.9539110381427958 | 1.2746887600915002 | 2.085495645578796 |
| user_003 | Walking | 1.446047694212e12 | -1.68549 | -8.39155 | 0.995729 | -3.0232225454545447 | -8.931517272727273 | -0.272325 | 2.8408765400999996 | 70.4181114025 | 0.991476241441 | 8.616870904454878 | 9.798154136854562 | 1.3377325454545448 | 0.5399672727272726 | 1.268054 | 1.7348647107438018 | 0.6739317355371907 | 1.7291648925619834 | 1.7895283631682957 | 0.29156465561652883 | 1.6079609469160001 | 5.105803910511268 | 0.6571788245633365 | 3.537602261767337 | 2.259602600129339 | 0.8106656675617493 | 1.8808514725430439 |
| user_003 | Walking | 1.446047694665e12 | -2.87342 | -9.31124 | -1.26517 | -2.444933818181818 | -9.474969999999999 | 0.41395727272727273 | 8.2565424964 | 86.6991903376 | 1.6006551288999997 | 9.826311004792185 | 9.933644266781107 | 0.42848618181818177 | 0.16372999999999927 | 1.6791272727272726 | 1.1565773719008263 | 0.6859657024793389 | 1.47200647107438 | 0.18360040800912392 | 2.680751289999976e-2 | 2.8194683980165283 | 1.6581222852926367 | 1.0010658402313288 | 2.4672273237565943 | 1.287680971860902 | 1.000532778189365 | 1.5707410110379731 |
| user_003 | Walking | 1.446047695248e12 | -3.25663 | -8.65979 | 0.727487 | -3.033673545454545 | -9.036027272727273 | -1.1223383636363635 | 10.6056389569 | 74.99196284409999 | 0.529237335169 | 9.28045468369783 | 9.887957901050113 | 0.22295645454545499 | 0.37623727272727336 | 1.8498253636363635 | 1.4678886198347103 | 1.2683712396694213 | 1.649039 | 4.9709580623479535e-2 | 0.1415544853892567 | 3.4218538759524044 | 4.119139327655233 | 2.252980369876934 | 3.138702060940305 | 2.029566290529884 | 1.500993127857997 | 1.7716382421195094 |
| user_004 | Jogging | 1.446046318092e12 | 5.59536 | -5.47921 | 4.55952 | 5.389392875 | -5.934259999999999 | -1.1645778749999995 | 31.308053529600002 | 30.021742224100002 | 20.7892226304 | 9.061954446150125 | 12.216535243673478 | 0.20596712499999992 | 0.45504999999999907 | 5.724097875 | 5.715480538541667 | 4.204357976190476 | 2.761588395684523 | 4.242245658076559e-2 | 0.20707050249999914 | 32.765296482579515 | 52.835246199173355 | 33.24591593153357 | 13.561606868199032 | 7.2687857444812165 | 5.765927152811902 | 3.6826087041931337 |
| user_004 | Jogging | 1.446046319776e12 | 0.575403 | 0.920286 | -2.4531 | 7.037601 | -5.81712190909091 | -0.8889327272727272 | 0.331088612409 | 0.8469263217960001 | 6.01769961 | 2.682482906600711 | 12.387770865882652 | 6.462198000000001 | 6.73740790909091 | 1.5641672727272729 | 6.405192909090909 | 5.481706280991736 | 3.3021975289256202 | 41.76000299120401 | 45.39266533348075 | 2.4466192570710747 | 61.41928641993893 | 42.6502568591944 | 14.266236440739085 | 7.837045771203517 | 6.530716412400281 | 3.777067174507105 |
| user_004 | Jogging | 1.446046320682e12 | 15.40536 | -14.7144 | -4.9056 | 5.773029999999999 | -4.29127709090909 | -0.7077829999999999 | 237.3251167296 | 216.51356736 | 24.064911359999996 | 21.86100627715019 | 11.59551959765128 | 9.63233 | 10.42312290909091 | 4.197817 | 7.104774851239669 | 5.092802545454545 | 2.930712619834711 | 92.78178122889999 | 108.64149117801576 | 17.621667565489 | 68.18608075165695 | 45.272654416877344 | 13.356822954340474 | 8.257486345835332 | 6.728495702374889 | 3.6546987501489743 |
| user_004 | Jogging | 1.44604632094e12 | -7.89339 | -0.229323 | 6.89706 | 5.720775454545455 | -4.859112818181817 | -0.33851381818181814 | 62.3056056921 | 5.2589038329e-2 | 47.5694366436 | 10.484637875197645 | 12.382847689240384 | 13.614165454545455 | 4.629789818181817 | 7.235573818181818 | 7.7625545206611575 | 5.522876561983471 | 3.032688570247934 | 185.34550102373888 | 21.434953760540022 | 52.35352847835821 | 85.137659610978 | 47.519657698112105 | 15.47308031707065 | 9.227007077648635 | 6.893450347838309 | 3.9335836481598623 |
| user_004 | Jogging | 1.44604632107e12 | 8.50771 | -12.98999 | 2.18366 | 6.323450000000002 | -5.085551 | -1.3731619999999998 | 72.3811294441 | 168.73984020010002 | 4.768370995600001 | 15.680859052991964 | 12.889850411129373 | 2.1842599999999974 | 7.904439000000001 | 3.556822 | 7.966191876033057 | 5.821204644628099 | 2.5582766694214873 | 4.770991747599989 | 62.480155904721016 | 12.650982739684 | 87.93005493205713 | 47.961374134025576 | 11.119942162986327 | 9.377102693905892 | 6.925415087489383 | 3.3346577280114262 |
| user_004 | Jogging | 1.4460463223e12 | 7.62634 | -1.11069 | -7.7239e-2 | 6.981861818181819 | -5.113419999999999 | -0.8053254545454547 | 58.1610617956 | 1.2336322760999998 | 5.965863121e-3 | 7.707182360293611 | 13.166977051043297 | 0.6444781818181813 | 4.002729999999999 | 0.7280864545454546 | 7.87466743801653 | 5.880742809917354 | 2.431282611570248 | 0.4153521268396687 | 16.02184745289999 | 0.5301098852925703 | 96.87531135707144 | 50.59894899318002 | 9.012174346215128 | 9.8425256594571 | 7.113293821653933 | 3.002028371987035 |
| user_004 | Jogging | 1.446046322495e12 | 12.4547 | -8.73643 | -4.40743 | 5.699873636363636 | -4.232053636363636 | -1.1153708181818183 | 155.11955209 | 76.3252091449 | 19.425439204899998 | 15.838882550224305 | 12.631746355363127 | 6.754826363636365 | 4.504376363636364 | 3.2920591818181815 | 8.404817429752066 | 5.924762809917355 | 2.7349940082644633 | 45.62767920287688 | 20.289406425285957 | 10.837653656593394 | 99.4303642243738 | 46.63110414598136 | 12.545256132475203 | 9.971477534667258 | 6.828697104571366 | 3.5419283070772627 |
| user_004 | Jogging | 1.446046322818e12 | 2.22318 | 4.714 | -0.690364 | 6.887803636363637 | -5.479206363636364 | -0.9342202727272728 | 4.9425293124000005 | 22.221796000000005 | 0.476602452496 | 5.257464005097515 | 13.652307201253643 | 4.664623636363637 | 10.193206363636364 | 0.24385627272727284 | 7.565568826446282 | 6.1325166942148766 | 3.4842991157024787 | 21.758713668922322 | 103.90145597167687 | 5.9465881748438074e-2 | 85.44219569961149 | 49.359338763878434 | 17.138670611506832 | 9.243494777388664 | 7.025620169342948 | 4.139887753491251 |
| user_004 | Jogging | 1.446046323919e12 | -12.14694 | 3.90927 | 0.152682 | 8.263850909090909 | -4.988009090909092 | -1.6344380000000003 | 147.54815136360003 | 15.282391932899998 | 2.3311793124000002e-2 | 12.761420574905602 | 13.32878135637509 | 20.41079090909091 | 8.897279090909091 | 1.7871200000000003 | 7.503497272727272 | 5.000009421487603 | 2.6817890330578518 | 416.6003855346281 | 79.1615752215281 | 3.1937978944000007 | 83.64378627905913 | 43.36608320371898 | 10.340309980211263 | 9.145697692306427 | 6.585292947448806 | 3.215635237431519 |
| user_004 | Jogging | 1.446046324307e12 | 3.14286 | -4.06135 | -2.41478 | 7.974707272727273 | -5.670806363636364 | -0.7391351818181818 | 9.877568979600001 | 16.4945638225 | 5.8311624484 | 5.674794732014543 | 13.21582437877326 | 4.831847272727273 | 1.6094563636363644 | 1.675644818181818 | 7.802776611570248 | 4.166146611570248 | 3.0396570413223145 | 23.34674806696199 | 2.5903497864495892 | 2.807785556699578 | 88.72848169522757 | 33.40847150884982 | 14.554685959196457 | 9.419579698438119 | 5.7800061858833525 | 3.8150604135709902 |
| user_004 | Jogging | 1.446046326574e12 | 12.0715 | -5.70913 | -2.29982 | 7.38248581818182 | -5.493142727272727 | -0.5266323636363637 | 145.72111225 | 32.5941653569 | 5.2891720324 | 13.55007194221861 | 13.15136563242288 | 4.68901418181818 | 0.21598727272727292 | 1.7731876363636363 | 7.681798446280993 | 5.468403966942149 | 2.036678347107438 | 21.98685399729202 | 4.665050198016537e-2 | 3.1441943937528594 | 85.34078258003082 | 48.436344589920964 | 6.831764064025652 | 9.238007500539865 | 6.959622445932032 | 2.6137643474547687 |
| user_004 | Jogging | 1.446046326639e12 | 1.61005 | 0.268841 | -2.8363 | 7.281459454545455 | -6.033110818181819 | -0.7112664545454546 | 2.5922610025 | 7.2275483281e-2 | 8.04459769 | 3.2724813484237 | 12.828283111638287 | 5.671409454545455 | 6.301951818181819 | 2.1250335454545457 | 7.794859388429751 | 5.05574826446281 | 2.2292298016528926 | 32.16488520110758 | 39.71459671868514 | 4.515767569307116 | 86.48260182948019 | 41.36213986419955 | 7.242283975172693 | 9.299602240390724 | 6.431340440701265 | 2.6911491922917787 |
| user_004 | Jogging | 1.44604632761e12 | 15.32872 | -14.67608 | -5.0972 | 5.4734360909090904 | -3.793111272727273 | -0.34896536363636366 | 234.96965683840003 | 215.38732416640002 | 25.98144784 | 21.8251787815083 | 11.905846007617221 | 9.855283909090911 | 10.882968727272727 | 4.7482346363636365 | 7.350532371900827 | 5.82310479338843 | 3.0558068677685952 | 97.12662092878622 | 118.43900831879617 | 22.545732161963315 | 77.70046956614337 | 45.631422866974525 | 15.833528605261323 | 8.814786983594292 | 6.755103468265643 | 3.9791366658185194 |
| user_004 | Jogging | 1.446046327869e12 | -10.65245 | 4.13919 | 5.36425 | 6.501117909090909 | -4.55603409090909 | -0.4186382727272728 | 113.4746910025 | 17.1328938561 | 28.7751780625 | 12.624688626698878 | 12.71974471624847 | 17.15356790909091 | 8.69522409090909 | 5.782888272727273 | 7.723284404958677 | 6.096096619834711 | 2.903793123966942 | 294.24489201179347 | 75.6069219911258 | 33.441796774846615 | 82.30222588031641 | 49.14387324683357 | 13.352193441058244 | 9.072057422675211 | 7.010269127988852 | 3.654065330704727 |
| user_004 | Jogging | 1.446046327934e12 | 12.22478 | -6.05401 | -4.63736 | 6.678785181818182 | -3.8593013636363636 | -1.0247964545454544 | 149.4452460484 | 36.6510370801 | 21.505107769600002 | 14.408379190530072 | 12.460816653562178 | 5.545994818181819 | 2.1947086363636363 | 3.6125635454545457 | 7.868965173553719 | 5.46998726446281 | 2.945280504132232 | 30.75805852329958 | 4.816745998529132 | 13.050615369947117 | 83.68466370606724 | 42.08347585402162 | 13.633011805578548 | 9.147932209306497 | 6.487177803484472 | 3.6922908614542473 |
| user_004 | Jogging | 1.446046328581e12 | -3.17999 | -1.68549 | -0.1922 | 8.981486363636364 | -6.047043636363636 | -1.160660090909091 | 10.1123364001 | 2.8408765400999996 | 3.694084e-2 | 3.604185591808502 | 13.103828097993564 | 12.161476363636364 | 4.361553636363636 | 0.9684600909090909 | 6.4954519752066116 | 5.569113595041323 | 2.5399093471074377 | 147.90150734328597 | 19.02315012287686 | 0.9379149476836446 | 54.42817332005659 | 46.03700027441227 | 9.23046015355527 | 7.377545209624715 | 6.785057131256322 | 3.0381672359426286 |
| user_004 | Jogging | 1.446046328905e12 | 19.50564 | -5.67081 | 1.03405 | 7.100308181818182 | -4.7511209090909094 | -0.4221227272727273 | 380.4699918096 | 32.158086056100004 | 1.0692594024999997 | 20.33955105866892 | 13.054363658769024 | 12.405331818181818 | 0.9196890909090909 | 1.4561727272727272 | 8.1137726446281 | 5.9206470247933884 | 2.656452793388429 | 153.8922575191942 | 0.84582802393719 | 2.120439011652892 | 88.05080876348978 | 50.20722639022751 | 9.792734096364068 | 9.383539245055129 | 7.08570577925922 | 3.1293344494259587 |
| user_004 | Jogging | 1.446046329617e12 | 3.56439 | -7.05034 | 6.05401 | 5.84618909090909 | -4.925304545454545 | -0.7042981818181816 | 12.7048760721 | 49.70729411560001 | 36.6510370801 | 9.953050148964387 | 12.291283422299722 | 2.28179909090909 | 2.125035454545455 | 6.758308181818181 | 6.877387685950413 | 6.140116363636364 | 2.6222487603305784 | 5.20660709127355 | 4.5157756830752085 | 45.67472948043057 | 77.1762593682838 | 52.764850806034104 | 10.386124266575505 | 8.785001956077403 | 7.263941822869598 | 3.2227510401170467 |
| user_004 | Jogging | 1.446046329683e12 | 19.00747 | -7.08866 | -0.1922 | 6.992314545454545 | -4.904402727272727 | -0.44650727272727264 | 361.28391580090005 | 50.2491005956 | 3.694084e-2 | 20.28718702128267 | 13.209857240769736 | 12.015155454545457 | 2.1842572727272733 | 0.2543072727272726 | 7.863264297520661 | 6.199972148760332 | 2.42336347107438 | 144.36396059689346 | 4.770979833461986 | 6.467218896198342e-2 | 90.17570123666434 | 52.98692180883688 | 9.849859374475129 | 9.496088733613663 | 7.279211620006447 | 3.1384485617061064 |
| user_004 | Jogging | 1.446046330071e12 | 17.05314 | -7.05034 | -3.75599 | 7.476543181818181 | -5.385147272727274 | -0.7356514545454546 | 290.80958385959997 | 49.70729411560001 | 14.107460880100001 | 18.831472031025616 | 13.604796241409561 | 9.576596818181818 | 1.6651927272727267 | 3.0203385454545457 | 6.897656694214876 | 5.849389256198347 | 2.7007900661157027 | 91.71120661801011 | 2.7728668189619814 | 9.122444929158481 | 82.83997545198206 | 51.75787714759219 | 10.641467584078235 | 9.101646853838158 | 7.194294763741071 | 3.2621262366864707 |
| user_004 | Jogging | 1.446046330265e12 | 4.02423 | -3.52487 | -1.76333 | 6.295580454545455 | -4.420171818181819 | -1.073566909090909 | 16.1944270929 | 12.424708516899999 | 3.1093326889000004 | 5.632802881221745 | 11.50053752911144 | 2.2713504545454546 | 0.8953018181818191 | 0.689763090909091 | 6.918558636363636 | 5.134288347107438 | 2.0914646446280996 | 5.159032887363844 | 0.8015653456396711 | 0.475773121580463 | 77.31788883703922 | 43.5971327069604 | 7.869029743331553 | 8.793059128485332 | 6.602812484612932 | 2.8051790929157363 |
| user_004 | Jogging | 1.446046330329e12 | 12.18646 | -8.04667 | 4.48288 | 7.079405 | -4.510747272727273 | -1.2163969090909088 | 148.5098073316 | 64.7488980889 | 20.0962130944 | 15.27595884109734 | 11.984438319305344 | 5.107055 | 3.5359227272727276 | 5.699276909090909 | 7.175400082644629 | 5.26255082644628 | 1.995189074380165 | 26.082010773024997 | 12.502749533243804 | 32.48175728649682 | 79.21565280810752 | 44.32322123879391 | 6.669668634792123 | 8.900317567823494 | 6.657568718293031 | 2.582570160671753 |
| user_004 | Jogging | 1.446046330848e12 | 16.01849 | -5.05768 | -2.33814 | 8.019996363636364 | -5.001943636363635 | -0.8088077272727273 | 256.5920218801 | 25.580126982400003 | 5.466898659600001 | 16.95992474989497 | 12.894361379172755 | 7.998493636363635 | 5.573636363636503e-2 | 1.529332272727273 | 6.6452502066115695 | 5.332539917355372 | 2.045543966942149 | 63.975900450949574 | 3.1065422314051137e-3 | 2.3388572004051658 | 71.60663987257274 | 44.567811622551844 | 7.075141314559144 | 8.462070661048202 | 6.675912793210517 | 2.6599137795348073 |
| user_004 | Jogging | 1.446046331495e12 | 8.62267 | -8.96635 | -0.460442 | 6.427961 | -3.901106363636364 | -0.32458081818181816 | 74.35043792889999 | 80.3954323225 | 0.21200683536400003 | 12.448207786133874 | 11.40122148597141 | 2.1947089999999996 | 5.065243636363636 | 0.13586118181818185 | 6.147401809917355 | 4.683945950413223 | 1.9023978347107438 | 4.816747594680998 | 25.65669309572231 | 1.845826072503307e-2 | 68.3485394276605 | 34.49821943043216 | 5.597293831373793 | 8.267317547285849 | 5.873518488132318 | 2.3658600616633674 |
| user_004 | Jogging | 1.446046331561e12 | 18.04947 | -17.93331 | -5.40376 | 6.612595545454545 | -5.071618181818182 | -0.6032735454545455 | 325.7833672809 | 321.6036075561 | 29.2006221376 | 26.01129748733423 | 12.22407355301134 | 11.436874454545453 | 12.861691818181818 | 4.800486454545455 | 6.45998188429752 | 5.848123719008263 | 2.1997754876033055 | 130.80209728903435 | 165.42311642588513 | 23.044670200274393 | 74.42364823112277 | 49.53640214712795 | 7.47964046772554 | 8.626914177799774 | 7.038210152242398 | 2.734893136436146 |
| user_004 | Jogging | 1.44604633318e12 | 18.31771 | -7.47186 | -3.10454 | 7.713432727272728 | -4.754604545454545 | -0.7042989090909091 | 335.53849964410006 | 55.828691859600006 | 9.638168611600001 | 20.02511822974586 | 13.149988530002542 | 10.604277272727273 | 2.7172554545454553 | 2.400241090909091 | 6.938827685950414 | 5.259700247933884 | 3.0469385041322314 | 112.45069647688018 | 7.383477205257028 | 5.761157294488465 | 77.11467456979197 | 48.156471003066486 | 12.895906901866105 | 8.78149614643154 | 6.939486364498923 | 3.5910871476289885 |
| user_004 | Jogging | 1.446046334476e12 | 6.13185 | -5.93905 | -3.0279 | 7.685562727272727 | -6.245612363636363 | -0.4499909090909093 | 37.5995844225 | 35.2723149025 | 9.16817841 | 9.05759779052923 | 13.862535036033972 | 1.5537127272727274 | 0.30656236363636324 | 2.5779090909090905 | 7.415138760330578 | 5.167541801652892 | 3.0304713057851242 | 2.4140232388892566 | 9.398048279831381e-2 | 6.645615280991733 | 82.42637556285928 | 43.10521410638309 | 12.187500293637099 | 9.078897265794964 | 6.565456123254735 | 3.4910600529978137 |
| user_004 | Jumping | 1.446046440753e12 | 19.54396 | -19.58108 | -6.89825 | 4.1884622857142855 | -2.999333571428571 | -1.5224617142857144 | 381.96637248159993 | 383.4186939664 | 47.5858530625 | 28.512644905559004 | 7.446083883383553 | 15.355497714285713 | 16.581746428571428 | 5.375788285714286 | 3.529288130612245 | 4.4176791921768706 | 1.270878112244898 | 235.79131005343376 | 274.9543146214413 | 28.899099692822944 | 40.42154505965868 | 49.56948207287595 | 4.601596063441028 | 6.357794040361695 | 7.040559784056659 | 2.14513311089103 |
| user_004 | Jumping | 1.446046441725e12 | 0.767005 | 1.30349 | 1.03405 | 3.717668909090908 | -2.779365454545454 | -0.7217180909090909 | 0.5882966700250001 | 1.6990861801000001 | 1.0692594024999997 | 1.832114148361122 | 6.847435681349492 | 2.950663909090908 | 4.082855454545454 | 1.7557680909090907 | 4.667160314049585 | 5.080450140495867 | 1.426719958677686 | 8.706417504411638 | 16.669708662711567 | 3.082721589054553 | 34.66505868718512 | 41.30950542823461 | 3.54571694005584 | 5.887704025100542 | 6.427247111184897 | 1.8830074190124264 |
| user_004 | Jumping | 1.446046441855e12 | 19.50564 | -19.58108 | -6.28513 | 6.448862999999999 | -5.51752609090909 | -1.188529 | 380.4699918096 | 383.4186939664 | 39.5028591169 | 28.34416244825202 | 10.589792564338788 | 13.056777 | 14.06355390909091 | 5.096601 | 6.050492041322314 | 6.463148644628098 | 1.8384256611570249 | 170.479425627729 | 197.7835485539062 | 25.975341753200997 | 53.52722088151006 | 62.083623176617515 | 5.872172381026635 | 7.31622996368417 | 7.879316161737484 | 2.423256565249878 |
| user_004 | Jumping | 1.446046442243e12 | 0.575403 | 1.15021 | -0.690364 | 3.6410292727272737 | -2.856005545454545 | -0.826227181818182 | 0.331088612409 | 1.3229830441 | 0.476602452496 | 1.4596828796026209 | 7.0567311800225605 | 3.0656262727272736 | 4.006215545454545 | 0.13586318181818202 | 4.304226818181818 | 5.246715801652892 | 1.354511909090909 | 9.398064444035716 | 16.049762996641654 | 1.8458804173760387e-2 | 29.45904764510686 | 39.08100872537067 | 3.7849847946391066 | 5.427618966462814 | 6.251480522673862 | 1.9455037380172537 |
| user_004 | Jumping | 1.446046442372e12 | 18.16443 | -17.3585 | 1.60885 | 5.591881181818182 | -4.914851 | -0.906352 | 329.9465172249 | 301.31752224999997 | 2.5883983225 | 25.17642623164376 | 9.544468036485442 | 12.572548818181819 | 12.443649 | 2.515202 | 5.156457942148761 | 5.815502611570248 | 1.753866247933884 | 158.06898378556505 | 154.844400435201 | 6.326241100803999 | 42.76716719331571 | 50.12337746871192 | 4.928973948004476 | 6.539661091625139 | 7.079786541182716 | 2.2201292638052577 |
| user_004 | Jumping | 1.446046443215e12 | -0.880768 | -0.612526 | -1.91661 | 4.623420909090909 | -3.2287572727272735 | -1.8016529999999997 | 0.775752269824 | 0.375188100676 | 3.6733938920999996 | 2.196436719461774 | 8.431501330251807 | 5.504188909090908 | 2.6162312727272736 | 0.1149570000000002 | 6.099263876033058 | 4.956621512396695 | 1.8605928016528923 | 30.296095546959364 | 6.84466607239617 | 1.3215111849000045e-2 | 44.71450237822124 | 32.7193750866241 | 6.223155739080556 | 6.686890336936986 | 5.720085234209723 | 2.494625370487632 |
| user_004 | Jumping | 1.446046444575e12 | 0.881966 | -4.94272 | 3.79311 | 6.790261454545456 | -6.263030272727272 | -0.8749987272727274 | 0.7778640251560001 | 24.430480998399997 | 14.387683472099999 | 6.292537524374089 | 11.126299442112229 | 5.908295454545455 | 1.3203102727272729 | 4.6681087272727275 | 6.9910826033057845 | 6.443830049586777 | 1.6366885537190081 | 34.90795517820249 | 1.7432192162691655 | 21.791239089639802 | 58.32619930429955 | 59.72487196810328 | 3.9530999690558626 | 7.637159112150247 | 7.728186848679532 | 1.9882404203354942 |
| user_004 | Jumping | 1.446046444769e12 | 0.268841 | -1.95374 | 3.7722e-2 | 3.2195042727272734 | -2.3926784545454542 | -0.9725412727272725 | 7.2275483281e-2 | 3.8170999876000002 | 1.422949284e-3 | 1.9725106894932154 | 7.197199404975865 | 2.9506632727272732 | 0.4389384545454542 | 1.0102632727272725 | 5.604267272727273 | 5.468403644628099 | 1.521094487603306 | 8.706413749021623 | 0.1926669668787518 | 1.0206318802216192 | 38.98070197949247 | 47.5268644494463 | 3.7172557078802515 | 6.243452729018814 | 6.893973052561658 | 1.9280185963522891 |
| user_004 | Jumping | 1.446046445287e12 | 0.11556 | -1.11069 | 0.382604 | 3.397171909090909 | -2.2568159090909092 | -0.9237708181818182 | 1.3354113599999998e-2 | 1.2336322760999998 | 0.146385820816 | 1.180411881724341 | 7.075012334908058 | 3.281611909090909 | 1.1461259090909093 | 1.3063748181818182 | 4.881565661157025 | 5.449401462809918 | 1.427669438016529 | 10.768976721887281 | 1.3136045994894632 | 1.7066151655795787 | 33.09439934785701 | 45.55143298526623 | 2.6018435675532396 | 5.752773187590226 | 6.749180171344237 | 1.6130231143890157 |
| user_004 | Jumping | 1.446046445611e12 | 2.8363 | -10.65245 | 1.80046 | 6.518536181818181 | -6.203808181818181 | -0.9899598181818184 | 8.04459769 | 113.4746910025 | 3.2416562115999996 | 11.16964390229608 | 10.831203183334122 | 3.682236181818181 | 4.448641818181819 | 2.7904198181818183 | 6.020091264462809 | 6.157535694214877 | 1.3522956859504134 | 13.558863298690936 | 19.790414026476036 | 7.786442761701852 | 50.54368804665865 | 54.31564822609823 | 2.9312400316853586 | 7.1094084174886625 | 7.36991507590815 | 1.712086455669035 |
| user_004 | Jumping | 1.44604644574e12 | -0.765807 | 2.33814 | -1.64837 | 3.5330348181818176 | -2.9117445454545456 | -0.8122925454545453 | 0.586460361249 | 5.466898659600001 | 2.7171236568999997 | 2.9615000722183007 | 7.545395689822752 | 4.298841818181818 | 5.249884545454545 | 0.8360774545454546 | 4.913235181818182 | 5.647971008264463 | 1.154676611570248 | 18.480040977748757 | 27.561287740602477 | 0.6990255099992067 | 32.672137932914204 | 43.8247559736747 | 2.23586034254455 | 5.715954682545533 | 6.620026886174609 | 1.495279352677803 |
| user_004 | Jumping | 1.446046446387e12 | 1.30349 | -1.57053 | -1.76333 | 3.4668452727272734 | -2.702724454545454 | -0.9307371818181818 | 1.6990861801000001 | 2.4665644809 | 3.1093326889000004 | 2.6972177053215414 | 6.522886174272082 | 2.1633552727272733 | 1.132194454545454 | 0.8325928181818183 | 4.403352826446281 | 4.768820842975207 | 1.425451826446281 | 4.680106036036896 | 1.2818642829034783 | 0.6932108008879423 | 29.814088068487422 | 33.13063707336669 | 2.8420858909499063 | 5.460227840345806 | 5.755921913418101 | 1.6858487153211306 |
| user_004 | Jumping | 1.446046446712e12 | -2.10702 | -2.18366 | 4.06135 | 5.431632909090909 | -5.245799181818182 | -0.7913907272727272 | 4.439533280399999 | 4.768370995600001 | 16.4945638225 | 5.069760161832116 | 9.726178424784191 | 7.538652909090908 | 3.0621391818181816 | 4.852740727272727 | 6.073928570247935 | 6.269962735537191 | 2.0376260165289257 | 56.83128768374481 | 9.376696368826122 | 23.54909256613144 | 51.70801020713879 | 53.619849938282414 | 5.52751424180559 | 7.190828200363209 | 7.322557609079113 | 2.3510666179003925 |
| user_004 | Jumping | 1.446046447294e12 | 0.690364 | 6.13185 | -2.41478 | 4.372599 | -3.9324581818181814 | -0.8088090909090909 | 0.476602452496 | 37.5995844225 | 5.8311624484 | 6.626262092869252 | 9.25374689473398 | 3.6822350000000004 | 10.06430818181818 | 1.605970909090909 | 5.767999487603306 | 6.21485658677686 | 1.7959872231404963 | 13.558854595225002 | 101.29029917861237 | 2.5791425608462806 | 46.44946369914939 | 57.41211919025554 | 6.140247961835453 | 6.815384339796942 | 7.577078539269309 | 2.477952372794008 |
| user_004 | Jumping | 1.446046447618e12 | 14.94552 | -13.18159 | -4.67568 | 4.574651181818182 | -3.8662689090909086 | -1.0700833636363638 | 223.36856807040002 | 173.7543149281 | 21.861983462399998 | 20.469119826238256 | 8.255475939941938 | 10.370868818181819 | 9.31532109090909 | 3.605596636363636 | 5.605217123966942 | 5.653037636363636 | 1.7095292644628097 | 107.55492004393595 | 86.77520702673573 | 13.000327104156765 | 46.00866792942744 | 48.25952326499671 | 5.097796350635938 | 6.782968961260802 | 6.946907460517717 | 2.2578300092424888 |
| user_004 | Jumping | 1.446046449302e12 | 19.08411 | -17.97163 | -4.06255 | 6.292096636363637 | -5.238832727272728 | -1.3313578181818182 | 364.2032544921 | 322.97948485690006 | 16.5043125025 | 26.527100328748713 | 10.742951779313438 | 12.79201336363636 | 12.732797272727273 | 2.731192181818182 | 6.28168061983471 | 6.237977859504133 | 1.770017694214876 | 163.63560589545125 | 162.12412638837108 | 7.459410734024761 | 57.181595329683844 | 55.44002913204303 | 5.704743067659652 | 7.561851316290466 | 7.445806143866696 | 2.3884603969209226 |
| user_004 | Jumping | 1.446046449884e12 | 9.77228 | -10.88237 | 0.305964 | 6.661365181818183 | -5.935567 | -1.4289012727272727 | 95.4974563984 | 118.4259768169 | 9.361396929600001e-2 | 14.629321487498865 | 10.730218018448529 | 3.1109148181818176 | 4.946803 | 1.7348652727272729 | 5.927614074380164 | 5.7094111404958685 | 1.4390697024793389 | 9.677791005983211 | 24.470859920809 | 3.009757514515075 | 50.27496977080698 | 50.6429279554915 | 3.326290719735871 | 7.090484452476218 | 7.1163844721523795 | 1.823812139376167 |
| user_004 | Jumping | 1.446046449949e12 | -2.0687 | 0.575403 | 1.07237 | 5.222611545454545 | -4.946205818181818 | -1.0387312727272728 | 4.279519690000001 | 0.331088612409 | 1.1499774169 | 2.4001220217541026 | 9.358456768935065 | 7.291311545454546 | 5.521608818181818 | 2.111101272727273 | 5.7591315454545455 | 5.5906493801652895 | 1.4365365619834711 | 53.16322405287876 | 30.488163941023213 | 4.456748583710711 | 47.505802565996596 | 49.17626807639828 | 3.315524702677734 | 6.892445325571803 | 7.012579274161418 | 1.8208582324491203 |
| user_004 | Jumping | 1.446046450273e12 | 1.03525 | 0.996927 | -2.33814 | 3.6863156363636365 | -2.5598956363636356 | -1.1815612727272728 | 1.0717425625 | 0.993863443329 | 5.466898659600001 | 2.7445408842698993 | 6.7919229626582425 | 2.6510656363636365 | 3.5568226363636355 | 1.1565787272727273 | 4.373900206611571 | 4.6421415041322325 | 1.104005090909091 | 7.028149008308133 | 12.650987266548762 | 1.3376743523798016 | 30.204080410236873 | 35.12321301457914 | 2.2349330079064456 | 5.495823906407198 | 5.926484034786489 | 1.4949692330969375 |
| user_004 | Jumping | 1.446046450403e12 | 15.2904 | -16.4005 | -3.44943 | 6.609110272727273 | -5.649906818181818 | -1.5125094545454545 | 233.79633216 | 268.97640025000004 | 11.8985673249 | 22.686368147742378 | 10.73749835209278 | 8.681289727272727 | 10.750593181818182 | 1.9369205454545455 | 5.950099537190083 | 6.178438495867769 | 1.3551456363636365 | 75.36479132885098 | 115.57525376095559 | 3.751661199403934 | 52.523254056726394 | 57.86450307308849 | 2.7311461983206873 | 7.247292877807989 | 7.6068720952234035 | 1.6526179831772034 |
| user_004 | Jumping | 1.446046450531e12 | 0.422122 | 6.63001 | -3.37279 | 3.9545577272727273 | -3.2078577272727276 | -0.9237703636363637 | 0.178186982884 | 43.95703260010001 | 11.375712384100002 | 7.450565882339677 | 8.460411268528444 | 3.5324357272727274 | 9.837867727272728 | 2.4490196363636363 | 5.265083752066116 | 5.7806671487603305 | 1.6712091074380166 | 12.478102167312802 | 96.78364141931428 | 5.997697179294677 | 39.42287793470751 | 48.03158359135643 | 4.1075853388824015 | 6.278764045153115 | 6.9304822048221455 | 2.0267178735291207 |
| user_004 | Jumping | 1.446046450921e12 | 19.54396 | -18.12491 | -4.9056 | 6.191071363636365 | -5.245801363636364 | -1.7528832727272723 | 381.96637248159993 | 328.5123625081 | 24.064911359999996 | 27.10246568763993 | 10.580288705372087 | 13.352888636363634 | 12.879108636363636 | 3.1527167272727272 | 6.375738710743801 | 6.294983049586777 | 1.9831561570247935 | 178.29963493512906 | 165.8714392672564 | 9.939622762425255 | 57.35020530279037 | 57.54984604088095 | 5.6981677095347285 | 7.572991833006977 | 7.586161482652538 | 2.3870835154084427 |
| user_004 | Jumping | 1.446046451179e12 | -0.689167 | -1.41725 | -0.537083 | 3.553937181818182 | -2.316038363636364 | -1.435869 | 0.47495115388899994 | 2.0085975625 | 0.28845814888899995 | 1.6649344927888305 | 7.489633255365871 | 4.2431041818181825 | 0.898788363636364 | 0.8987860000000001 | 4.800173760330579 | 5.271100983471074 | 1.8400087190082643 | 18.003933097762946 | 0.807820522608133 | 0.8078162737960002 | 35.33453433582328 | 40.798926170358726 | 4.5008850493141415 | 5.944285855830226 | 6.387403711239703 | 2.1215289414274183 |
| user_004 | Jumping | 1.446046451309e12 | 5.99e-4 | 2.22318 | -0.613724 | 3.790826000000001 | -3.0301892727272732 | -1.1258224545454545 | 3.5880100000000004e-7 | 4.9425293124000005 | 0.37665714817600005 | 2.306336232941112 | 7.049070078541021 | 3.790227000000001 | 5.253369272727273 | 0.5120984545454544 | 4.614273231404958 | 4.632957049586778 | 1.697811479338843 | 14.36582071152901 | 27.597888715635076 | 0.26224482714784286 | 34.150457092262734 | 33.510629728040335 | 4.005569173775021 | 5.843839242506824 | 5.788836647206443 | 2.0013918091605705 |
| user_004 | Jumping | 1.446046451762e12 | 0.920286 | -2.41358 | 0.344284 | 3.5051649090909085 | -2.3717772727272726 | -1.3940651818181817 | 0.8469263217960001 | 5.8253684164 | 0.11853147265599999 | 2.6059213746488976 | 7.351790595676024 | 2.5848789090909086 | 4.180272727272749e-2 | 1.7383491818181818 | 5.23436547107438 | 4.90373214876033 | 1.4349531900826447 | 6.681598974663006 | 1.7474680074380348e-3 | 3.021857877927942 | 39.88697797769975 | 39.20278665489539 | 2.894475197684697 | 6.315613824300829 | 6.2612128741079704 | 1.7013157254562414 |
| user_004 | Jumping | 1.446046451892e12 | -0.382604 | 0.690364 | 7.6042e-2 | 3.557419909090909 | -2.660921272727273 | -1.2129147272727272 | 0.146385820816 | 0.476602452496 | 5.782385764e-3 | 0.7929506031752546 | 6.9635497729535 | 3.9400239090909093 | 3.3512852727272726 | 1.2889567272727271 | 5.185593942148761 | 4.676977388429751 | 1.325692917355372 | 15.523788404208009 | 11.23111297919871 | 1.6614094447816194 | 39.5295646746675 | 36.15230679208021 | 2.620653828008208 | 6.287254144272164 | 6.0126788365985595 | 1.6188433611712432 |
| user_004 | Jumping | 1.446046452021e12 | 19.54396 | -19.08292 | -7.43474 | 6.243325454545455 | -5.102970363636363 | -1.8922294545454543 | 381.96637248159993 | 364.15783572640004 | 55.2753588676 | 28.30900152028679 | 10.349931781329445 | 13.300634545454542 | 13.979949636363639 | 5.542510545454546 | 6.506536148760332 | 5.7812990247933875 | 1.7316982231404958 | 176.90687931173878 | 195.43899183526383 | 30.719423146474846 | 58.50151760448834 | 53.12020252076061 | 5.359819226407735 | 7.648628478654741 | 7.288360756765584 | 2.315128339079226 |
| user_004 | Jumping | 1.446046452538e12 | 18.54763 | -15.40417 | -3.98591 | 4.9648212727272725 | -3.9847141818181813 | -1.3940644545454548 | 344.01457861690005 | 237.28845338890002 | 15.8874785281 | 24.437481673321006 | 8.936126658691224 | 13.58280872727273 | 11.41945581818182 | 2.5918455454545453 | 5.416781504132231 | 5.744247553719009 | 1.6895778347107437 | 184.49269292167622 | 130.4039711834066 | 6.717663331492569 | 45.55107155931277 | 48.760047922413534 | 5.977856426539219 | 6.749153395746222 | 6.982839531480981 | 2.444965526656607 |
| user_004 | Jumping | 1.446046452993e12 | 0.613724 | 2.10822 | -1.38013 | 3.665413818181818 | -2.7480122727272733 | -1.157175909090909 | 0.37665714817600005 | 4.444591568400001 | 1.9047588169000003 | 2.5934547486848505 | 7.334066225954348 | 3.051689818181818 | 4.856232272727274 | 0.22295409090909102 | 4.790039247933884 | 5.016162074380166 | 1.2848392396694217 | 9.312810746394577 | 23.582991886677906 | 4.970852665309922e-2 | 36.88789352842561 | 41.51884982458453 | 3.4438666456618092 | 6.073540444289937 | 6.443512227394662 | 1.8557657841607624 |
| user_004 | Jumping | 1.446046453445e12 | 3.0279 | -1.45557 | -7.7239e-2 | 3.675864363636364 | -2.6922731818181824 | -1.1362745454545455 | 9.16817841 | 2.1186840249000003 | 5.965863121e-3 | 3.3604803671530354 | 7.688581707506595 | 0.6479643636363641 | 1.2367031818181824 | 1.0590355454545455 | 5.11655426446281 | 4.447690247933884 | 1.4346366611570247 | 0.4198578165426783 | 1.5294347599192164 | 1.1215562865362068 | 38.29682912226736 | 35.61574287410279 | 4.090531801082638 | 6.1884431905179 | 5.967892666101058 | 2.0225063166978337 |
| user_004 | Jumping | 1.44604645351e12 | -7.6042e-2 | 0.11556 | -1.26517 | 3.8639823636363633 | -2.8873586363636368 | -1.0631172727272726 | 5.782385764e-3 | 1.3354113599999998e-2 | 1.6006551288999997 | 1.2727103473548094 | 7.464115552925086 | 3.9400243636363634 | 3.0029186363636367 | 0.20205272727272727 | 4.969606842975207 | 4.325445000000001 | 1.371297 | 15.523791986048131 | 9.017520336620043 | 4.082530459834711e-2 | 36.90134728909755 | 34.7171749005811 | 4.02080485908967 | 6.0746479148258095 | 5.892128214879671 | 2.005194469144993 |
| user_004 | Jumping | 1.446046453704e12 | 19.54396 | -17.70339 | -8.46939 | 6.215456545454546 | -4.782473272727273 | -1.665791818181818 | 381.96637248159993 | 313.41001749209994 | 71.73056697210001 | 27.69669577667704 | 10.319630313832954 | 13.328503454545451 | 12.920916727272726 | 6.803598181818183 | 6.116683256198346 | 5.222330438016529 | 1.9416679917355373 | 177.64900433783004 | 166.95008907311612 | 46.288948219639686 | 53.542354201833035 | 48.31052397701225 | 8.222915800069886 | 7.3172641199995665 | 6.950577240561553 | 2.8675626933111484 |
| user_004 | Jumping | 1.446046454158e12 | 0.652044 | 1.72501 | -1.03525 | 2.9965505454545447 | -2.4797716363636364 | -1.2582024545454547 | 0.42516137793599995 | 2.9756595001 | 1.0717425625 | 2.114843597180652 | 6.536228248549308 | 2.3445065454545446 | 4.204781636363636 | 0.22295245454545465 | 4.096157082644628 | 4.746651454545454 | 1.610085991735537 | 5.496710941679202 | 17.680188609500856 | 4.970779698784302e-2 | 26.08166873535217 | 35.08718337156006 | 6.381092379007582 | 5.107021513108416 | 5.923443539999353 | 2.5260824173030425 |
| user_004 | Jumping | 1.446046454222e12 | 18.04947 | -16.01729 | -3.67935 | 4.644324363636363 | -3.946394363636364 | -1.4776733636363637 | 325.7833672809 | 256.55357894409997 | 13.5376164225 | 24.410542039198965 | 8.639667493262413 | 13.405145636363637 | 12.070895636363636 | 2.2016766363636364 | 4.956622652892562 | 5.571013 | 1.7918699834710743 | 179.69792953211905 | 145.70652146398265 | 4.847380011109496 | 41.00659033044952 | 47.5134562013203 | 6.818051897781323 | 6.403638835103798 | 6.89300052236472 | 2.6111399613542976 |
| user_004 | Jumping | 1.446046454805e12 | 19.54396 | -19.54276 | -2.60638 | 5.665038272727272 | -4.580421272727273 | -1.592634181818182 | 381.96637248159993 | 381.91946841760006 | 6.793216704400001 | 27.761106923240654 | 10.240967917439297 | 13.878921727272726 | 14.962338727272728 | 1.0137458181818182 | 5.758814264462809 | 6.220874892561983 | 1.9220324628099172 | 192.62446831176294 | 223.87158018964527 | 1.027680583881124 | 52.80492603983436 | 63.4497100096025 | 6.33968770209533 | 7.266699803888582 | 7.965532625606557 | 2.5178736469678795 |
| user_004 | Jumping | 1.44604645487e12 | 12.22478 | -14.1396 | -1.03525 | 6.717105181818181 | -6.022658545454546 | -1.592634181818182 | 149.4452460484 | 199.92828816 | 1.0717425625 | 18.720183673535363 | 11.750544288016998 | 5.50767481818182 | 8.116941454545454 | 0.557384181818182 | 6.046375016528926 | 6.576525785123966 | 1.9524353471074383 | 30.334481902834142 | 65.88473857651847 | 0.3106771261411241 | 55.06290521812116 | 67.83194182478591 | 6.36341218656381 | 7.420438344068439 | 8.236014923783584 | 2.522580461861189 |
| user_004 | Jumping | 1.446046454999e12 | 1.11189 | 5.2888 | -2.49142 | 3.543486090909091 | -2.371778181818182 | -0.9168032727272727 | 1.2362993721000002 | 27.97140544 | 6.207173616400001 | 5.951040113165093 | 8.1335275932362 | 2.431596090909091 | 7.660578181818182 | 1.5746167272727276 | 4.734616950413223 | 5.498490280991735 | 2.0813304214876034 | 5.912659549324371 | 58.684458079748765 | 2.4794178378070755 | 34.00437737975152 | 47.93823792689663 | 7.181088815206571 | 5.831327239981608 | 6.923744501849893 | 2.679755364806006 |
| user_004 | Jumping | 1.446046455711e12 | 1.64837 | -0.650847 | -0.613724 | 3.362334090909091 | -2.775883545454545 | -1.0178284545454546 | 2.7171236568999997 | 0.42360181740899994 | 0.37665714817600005 | 1.8754686407628893 | 7.188170906724486 | 1.7139640909090912 | 2.1250365454545452 | 0.4041044545454545 | 4.886314818181819 | 4.410319421487603 | 1.4837242231404957 | 2.937672904925827 | 4.515780319517387 | 0.16330041018347932 | 35.089401725842805 | 36.05135667432639 | 3.7502605393120323 | 5.923630789122733 | 6.004278197612631 | 1.9365589428964025 |
| user_004 | Jumping | 1.446046455906e12 | 19.54396 | -18.43147 | -6.89825 | 5.710325181818182 | -4.698866090909092 | -1.634437181818182 | 381.96637248159993 | 339.7190863609 | 47.5858530625 | 27.735740695085102 | 9.91661877902701 | 13.833634818181817 | 13.732603909090908 | 5.263812818181818 | 5.815186834710744 | 5.599832173553719 | 1.9872717603305785 | 191.3694522828123 | 188.5844101239789 | 27.707725384855213 | 50.6927278488261 | 52.74739577233272 | 6.313621098201948 | 7.119882572685177 | 7.262740238527929 | 2.5126920022561356 |
| user_004 | Jumping | 1.44604645597e12 | 8.77595 | -13.18159 | 0.804128 | 6.455829454545454 | -6.026142454545454 | -1.568247545454546 | 77.0172984025 | 173.7543149281 | 0.646621840384 | 15.85617340883304 | 11.218761559924888 | 2.3201205454545457 | 7.155447545454546 | 2.372375545454546 | 5.748047107438017 | 5.903544363636365 | 2.0901981487603303 | 5.382959345440299 | 51.200429575751485 | 5.628165728670754 | 50.331598154741954 | 56.07913702398294 | 6.685448936200232 | 7.094476594840662 | 7.488600471649088 | 2.585623510142231 |
| user_004 | Jumping | 1.446046456423e12 | 15.78857 | -13.10495 | -5.59536 | 4.7453508181818185 | -3.9289750909090913 | -1.4672216363636366 | 249.2789426449 | 171.73971450250002 | 31.308053529600002 | 21.267973826319235 | 8.458796354972607 | 11.04321918181818 | 9.17597490909091 | 4.1281383636363636 | 4.97340756198347 | 5.5171746115702485 | 2.1848901239669414 | 121.952689897677 | 84.19851553226592 | 17.041526349326315 | 38.41901825757145 | 44.11913462622769 | 6.885287929257149 | 6.1983076930377905 | 6.6422236206128815 | 2.623983218173689 |
| user_004 | Jumping | 1.446046457136e12 | -0.842448 | -0.919089 | 3.48655 | 5.034495545454545 | -4.845178090909091 | -0.8018418181818183 | 0.7097186327039999 | 0.8447245899210001 | 12.1560309025 | 3.7027657399739726 | 9.638416900898887 | 5.876943545454545 | 3.9260890909090906 | 4.288391818181818 | 5.478537785123968 | 5.961183520661158 | 2.0959002066115704 | 34.538465436459845 | 15.41417554975537 | 18.39030438624876 | 46.56730431938241 | 52.07823859486755 | 7.056477213755475 | 6.824024056184328 | 7.216525382403054 | 2.6564030593559167 |
| user_004 | Standing | 1.446046405596e12 | 0.958607 | -9.4262 | 3.7722e-2 | 0.9620908181818184 | -9.380912727272724 | 0.17358445454545454 | 0.918927380449 | 88.85324643999999 | 1.422949284e-3 | 9.47489296877453 | 9.43237148191842 | 3.483818181818421e-3 | 4.528727272727551e-2 | 0.13586245454545454 | 6.277442520989121e-2 | 3.564091774891797e-2 | 8.517581524662207e-2 | 1.2136989123968609e-5 | 2.050937071074632e-3 | 1.84586065551157e-2 | 5.963056016724221e-3 | 1.957546352325278e-3 | 1.0145801756643359e-2 | 7.722082631469454e-2 | 4.424416743849157e-2 | 0.1007263707111666 |
| user_004 | Standing | 1.44604640592e12 | 0.920286 | -9.38788 | -5.99e-4 | 0.9551232727272727 | -9.380912727272726 | 7.952554545454546e-2 | 0.8469263217960001 | 88.13229089439999 | 3.5880100000000004e-7 | 9.432879601425908 | 9.430044911924979 | 3.483727272727266e-2 | 6.967272727273155e-3 | 8.012454545454546e-2 | 3.9903884297520766e-2 | 3.578644628099232e-2 | 8.487475206611572e-2 | 1.2136355710743755e-3 | 4.854288925620431e-5 | 6.4199427842975216e-3 | 2.228623198332091e-3 | 1.6228770476333844e-3 | 8.828314650245684e-3 | 4.720829586346124e-2 | 4.028494815229858e-2 | 9.395911158714564e-2 |
| user_004 | Standing | 1.446046406114e12 | 1.03525 | -9.38788 | -0.11556 | 0.9760256363636363 | -9.387879999999997 | 2.7270636363636358e-2 | 1.0717425625 | 88.13229089439999 | 1.3354113599999998e-2 | 9.445495623338141 | 9.439062086037746 | 5.922436363636374e-2 | 1.7763568394002505e-15 | 0.14283063636363635 | 5.573941322314055e-2 | 3.0085950413224018e-2 | 9.37422396694215e-2 | 3.507525248132244e-3 | 3.1554436208840472e-30 | 2.040059068404132e-2 | 4.640469144164543e-3 | 1.4684224000000342e-3 | 1.0355239224131481e-2 | 6.812098901340571e-2 | 3.8320000000000444e-2 | 0.10176069587090823 |
| user_004 | Standing | 1.446046406438e12 | 1.03525 | -9.34956 | -3.8919e-2 | 1.010862818181818 | -9.387879999999997 | -3.543536363636363e-2 | 1.0717425625 | 87.41427219360001 | 1.514688561e-3 | 9.40678103522459 | 9.4424099981621 | 2.4387181818182002e-2 | 3.831999999999702e-2 | 3.4836363636363693e-3 | 4.4338446280991796e-2 | 2.786909090909068e-2 | 6.872322314049586e-2 | 5.947346370330668e-4 | 1.4684223999997718e-3 | 1.2135722314049627e-5 | 3.3783190145950433e-3 | 1.381265848835436e-3 | 6.272074051694967e-3 | 5.812330870309297e-2 | 3.7165385089292916e-2 | 7.919642701343897e-2 |
| user_004 | Standing | 1.446046409221e12 | 1.07357 | -9.34956 | -7.7239e-2 | 1.0387325454545453 | -9.366978181818181 | -9.814136363636364e-2 | 1.1525525448999998 | 87.41427219360001 | 5.965863121e-3 | 9.411311842757152 | 9.4250039711814 | 3.4837454545454616e-2 | 1.741818181818111e-2 | 2.0902363636363636e-2 | 2.977019008264463e-2 | 3.895338842975155e-2 | 3.2303239669421495e-2 | 1.2136482392066164e-3 | 3.0339305785121503e-4 | 4.369088055867768e-4 | 1.2335131438189323e-3 | 2.1502293445529123e-3 | 1.3349670551164533e-3 | 3.5121405777943066e-2 | 4.6370565497445816e-2 | 3.653720097539566e-2 |
| user_004 | Standing | 1.446046409545e12 | 1.03525 | -9.46452 | -0.15388 | 1.0457003636363635 | -9.366978181818181 | -0.12252709090909092 | 1.0717425625 | 89.5771388304 | 2.3679054399999996e-2 | 9.52221405174763 | 9.426078761809261 | 1.0450363636363535e-2 | 9.754181818181884e-2 | 3.135290909090907e-2 | 2.7236024793388432e-2 | 3.2619504132230824e-2 | 2.881955371900826e-2 | 1.092101001322293e-4 | 9.514406294215004e-3 | 9.830049084628087e-4 | 1.2334795734898574e-3 | 1.730995300976674e-3 | 9.763978611780614e-4 | 3.512092785633457e-2 | 4.1605231653923935e-2 | 3.1247365667813685e-2 |
| user_004 | Standing | 1.446046410257e12 | 0.537083 | -9.54116 | -0.11556 | 1.0108632727272726 | -9.373945454545456 | -0.10510872727272727 | 0.28845814888899995 | 91.0337341456 | 1.3354113599999998e-2 | 9.556963241955522 | 9.432190682868356 | 0.47378027272727263 | 0.16721454545454328 | 1.0451272727272726e-2 | 0.12129503305785122 | 7.600661157024775e-2 | 6.999014049586777e-2 | 0.22446774682552884 | 2.7960704211569522e-2 | 1.0922910161983468e-4 | 4.670707975050865e-2 | 7.85070908970695e-3 | 8.712536485643125e-3 | 0.21611820781810276 | 8.860422726770405e-2 | 9.334096895599019e-2 |
| user_004 | Standing | 1.446046411099e12 | 0.843646 | -9.38788 | -0.11556 | 1.0770524545454547 | -9.363494545454545 | -0.15736363636363634 | 0.711738573316 | 88.13229089439999 | 1.3354113599999998e-2 | 9.426419446498016 | 9.42831270330459 | 0.23340645454545472 | 2.4385454545454266e-2 | 4.180363636363635e-2 | 0.15739903305785125 | 7.854016528925617e-2 | 6.492289256198348e-2 | 5.447857302347942e-2 | 5.946503933884161e-4 | 1.7475440132231391e-3 | 3.572726408331256e-2 | 8.139759930278025e-3 | 5.109235888016529e-3 | 0.18901657092253196 | 9.022061809962302e-2 | 7.147891918612459e-2 |
| user_004 | Standing | 1.446046411163e12 | 0.996927 | -9.27292 | -3.8919e-2 | 1.059634 | -9.36001090909091 | -0.13994527272727272 | 0.993863443329 | 85.98704532639998 | 1.514688561e-3 | 9.326436803961627 | 9.422568921205992 | 6.270699999999996e-2 | 8.709090909091088e-2 | 0.10102627272727271 | 0.14884828925619836 | 8.075702479338855e-2 | 6.428947107438017e-2 | 3.932167848999995e-3 | 7.584826446281304e-3 | 1.0206307781165287e-2 | 3.38506136849955e-2 | 8.471837422689771e-3 | 4.9768380135101434e-3 | 0.18398536269224108 | 9.204258483272713e-2 | 7.054670802744904e-2 |
| user_004 | Standing | 1.446046411228e12 | 1.11189 | -9.34956 | -0.307161 | 1.0526667272727273 | -9.353043636363637 | -0.14691254545454543 | 1.2362993721000002 | 87.41427219360001 | 9.434787992100001e-2 | 9.420452189020494 | 9.415007269715698 | 5.922327272727279e-2 | 3.4836363636365775e-3 | 0.16024845454545458 | 0.14061423140495868 | 7.727338842975212e-2 | 6.967328099173555e-2 | 3.507396032528933e-3 | 1.2135722314051077e-5 | 2.5679567184206623e-2 | 3.21295211817701e-2 | 8.314073032607122e-3 | 6.383492949667169e-3 | 0.17924709532310448 | 9.118153888045058e-2 | 7.989676432539161e-2 |
| user_004 | Standing | 1.446046411746e12 | 1.30349 | -9.38788 | -5.99e-4 | 1.0178297272727272 | -9.366978181818181 | -7.375572727272729e-2 | 1.6990861801000001 | 88.13229089439999 | 3.5880100000000004e-7 | 9.477941624282193 | 9.426680900683847 | 0.2856602727272728 | 2.090181818181769e-2 | 7.315672727272729e-2 | 0.19255234710743804 | 8.139041322314065e-2 | 0.12699536363636363 | 8.160179141461987e-2 | 4.368860033057645e-4 | 5.3519067452562005e-3 | 4.840703688697522e-2 | 1.0951937764688268e-2 | 2.426411914712021e-2 | 0.2200159923436822 | 0.104651506270518 | 0.15576944227646258 |
| user_004 | Standing | 1.446046413688e12 | 0.460442 | -9.50284 | -0.575403 | 1.2930383636363636 | -9.293821818181819 | -0.5196647272727272 | 0.21200683536400003 | 90.30396806560002 | 0.331088612409 | 9.531372593355744 | 9.407684402739001 | 0.8325963636363636 | 0.2090181818181822 | 5.573827272727283e-2 | 0.268874479338843 | 0.152963305785124 | 0.31067958677685953 | 0.6932167047404958 | 4.368860033057868e-2 | 3.106755046619846e-3 | 0.1280806416731991 | 3.583678799338847e-2 | 0.12849849671874833 | 0.35788355881934436 | 0.18930606961581678 | 0.3584668697644851 |
| user_004 | Standing | 1.446046414271e12 | 1.30349 | -9.50284 | -0.345482 | 1.2512353636363636 | -9.38787909090909 | -0.3977365454545454 | 1.6990861801000001 | 90.30396806560002 | 0.119357812324 | 9.598042095032925 | 9.491118177753593 | 5.2254636363636475e-2 | 0.11496090909091095 | 5.225454545454539e-2 | 0.2802762314049587 | 0.12826206611570223 | 0.18431735537190086 | 2.7305470214958796e-3 | 1.321601061900869e-2 | 2.73053752066115e-3 | 0.14207760969693764 | 2.8615357226371166e-2 | 7.562875789122314e-2 | 0.3769318369373137 | 0.16916074375093995 | 0.2750068324446197 |
| user_004 | Standing | 1.446046414789e12 | 1.34181 | -9.19628 | -3.8919e-2 | 1.293039090909091 | -9.335625454545454 | -0.4116710909090909 | 1.8004540760999999 | 84.57156583839999 | 1.514688561e-3 | 9.293736310174772 | 9.436144452768678 | 4.8770909090908976e-2 | 0.13934545454545422 | 0.3727520909090909 | 0.10735933057851237 | 0.10387586776859514 | 0.10799344628099172 | 2.3786015735537077e-3 | 1.9417155702479247e-2 | 0.13894412127709915 | 1.9197577323342603e-2 | 1.8777275617580767e-2 | 2.0440168707416224e-2 | 0.13855532224834455 | 0.13703019965533425 | 0.14296911801999837 |
| user_004 | Standing | 1.446046414919e12 | 0.728685 | -9.31124 | -0.728685 | 1.1885289090909092 | -9.363494545454545 | -0.4290895454545454 | 0.530981829225 | 86.6991903376 | 0.530981829225 | 9.36809233494472 | 9.455222723878217 | 0.4598439090909092 | 5.225454545454511e-2 | 0.2995954545454546 | 0.19065084297520657 | 0.13301173553719037 | 0.1374462561983471 | 0.21145642072800838 | 2.730537520661121e-3 | 8.975743638429756e-2 | 8.091771112459506e-2 | 3.0215736309241223e-2 | 3.0178694488630354e-2 | 0.28446038586171374 | 0.17382674221546357 | 0.17372016143392902 |
| user_004 | Standing | 1.446046415436e12 | 1.03525 | -9.34956 | -0.537083 | 1.324391636363636 | -9.321690909090908 | -0.49876290909090915 | 1.0717425625 | 87.41427219360001 | 0.28845814888899995 | 9.422020638110968 | 9.442251657405361 | 0.2891416363636361 | 2.786909090909262e-2 | 3.832009090909083e-2 | 0.33664782644628094 | 9.56416528925622e-2 | 0.20426942148760333 | 8.360288587904117e-2 | 7.766862280992689e-4 | 1.4684293672809858e-3 | 0.17132482064501955 | 1.848160183681444e-2 | 5.515776418483396e-2 | 0.41391402566839836 | 0.13594705527084594 | 0.23485690150564867 |
| user_004 | Standing | 1.446046416278e12 | 5.99e-4 | -8.88971 | 0.114362 | 1.0770511818181818 | -9.339108181818181 | -0.29322672727272725 | 3.5880100000000004e-7 | 79.02694388409998 | 1.3078667044000002e-2 | 8.890445596815999 | 9.41858010801273 | 1.0764521818181818 | 0.44939818181818225 | 0.40758872727272727 | 0.35945021487603307 | 0.13902958677685975 | 0.13681291735537188 | 1.158749299741124 | 0.201958725821488 | 0.16612857059980166 | 0.2264460329577461 | 4.479810685048842e-2 | 3.490508766509166e-2 | 0.47586346041458794 | 0.2116556326925613 | 0.18682903324989844 |
| user_004 | Standing | 1.446046416408e12 | 1.64837 | -9.11964 | 0.114362 | 1.1850444545454544 | -9.363493636363636 | -0.2618737272727273 | 2.7171236568999997 | 83.1678337296 | 1.3078667044000002e-2 | 9.26811933746777 | 9.456573244994104 | 0.4633255454545455 | 0.24385363636363522 | 0.3762357272727273 | 0.3648338347107438 | 0.19540115702479371 | 0.1659490743801653 | 0.21467056107075214 | 5.9464595967768034e-2 | 0.14155332247643804 | 0.21016706771208565 | 6.667774145319322e-2 | 4.803837257303833e-2 | 0.458439819073437 | 0.2582203350884535 | 0.21917657852297615 |
| user_004 | Standing | 1.446046416667e12 | 1.61005 | -9.27292 | -0.652044 | 1.1850441818181816 | -9.346075454545455 | -0.30367763636363637 | 2.5922610025 | 85.98704532639998 | 0.42516137793599995 | 9.434217917073783 | 9.441841544024516 | 0.4250058181818184 | 7.315545454545536e-2 | 0.3483663636363636 | 0.38985298347107433 | 0.18495024793388445 | 0.1963519338842975 | 0.18062994548839686 | 5.351720529752185e-3 | 0.1213591233132231 | 0.20996067308201585 | 6.1537693003906886e-2 | 5.803834992721262e-2 | 0.458214658301124 | 0.24806792014266352 | 0.2409114981216393 |
| user_004 | Standing | 1.446046416797e12 | 0.383802 | -9.2346 | -0.11556 | 1.153691636363636 | -9.363493636363636 | -0.23748799999999995 | 0.147303975204 | 85.27783716 | 1.3354113599999998e-2 | 9.243294610083788 | 9.460083489740654 | 0.769889636363636 | 0.12889363636363527 | 0.12192799999999995 | 0.4987971652892563 | 0.1906507438016531 | 0.23625571900826445 | 0.5927300521801318 | 1.661356949504104e-2 | 1.4866437183999989e-2 | 0.32266433426134267 | 6.264976186055603e-2 | 6.975061971639669e-2 | 0.5680355044020952 | 0.2502993445068445 | 0.26410342617315036 |
| user_004 | Standing | 1.446046417768e12 | 1.30349 | -9.31124 | -0.460442 | 1.0143450909090908 | -9.40529818181818 | -0.19568381818181815 | 1.6990861801000001 | 86.6991903376 | 0.21200683536400003 | 9.413303530273737 | 9.479346163800669 | 0.2891449090909093 | 9.405818181818049e-2 | 0.2647581818181819 | 0.4269073305785124 | 0.12572760330578509 | 0.3046622231404959 | 8.36047784531902e-2 | 8.846941566941898e-3 | 7.009689483966947e-2 | 0.23773179596939376 | 2.566705269421491e-2 | 0.11697479484084222 | 0.48757747688894915 | 0.16020940263984168 | 0.3420157815669362 |
| user_004 | Standing | 1.446046418286e12 | 1.45677 | -9.2346 | -0.805325 | 1.362711090909091 | -9.304272727272727 | -0.4499914545454546 | 2.1221788328999995 | 85.27783716 | 0.6485483556249999 | 9.383419651093359 | 9.425167218813767 | 9.405890909090897e-2 | 6.967272727272622e-2 | 0.3553335454545454 | 0.32904747107438015 | 0.12889454545454546 | 0.2796432561983471 | 8.847078379371879e-3 | 4.854288925619688e-3 | 0.12626192852529747 | 0.14820659277210974 | 2.504923410368146e-2 | 0.10975174829168598 | 0.38497609376701525 | 0.1582694983364813 | 0.33128801410809594 |
| user_004 | Standing | 1.446046418545e12 | 1.03525 | -9.65612 | -0.920286 | 1.3278747272727272 | -9.314723636363636 | -0.6032727272727273 | 1.0717425625 | 93.24065345439999 | 0.8469263217960001 | 9.754963984489947 | 9.439623906047471 | 0.29262472727272715 | 0.3413963636363633 | 0.31701327272727275 | 0.29642714876033055 | 0.14504595041322332 | 0.2695089669421488 | 8.562923101143795e-2 | 0.11655147710413198 | 0.10049741508525621 | 0.12480726170758606 | 3.192136267588283e-2 | 0.10582854600052521 | 0.35328071233452024 | 0.17866550499713937 | 0.325312996974491 |
| user_004 | Standing | 1.446046419257e12 | 0.728685 | -9.50284 | -0.690364 | 1.3243917272727275 | -9.339109090909092 | -0.5858543636363636 | 0.530981829225 | 90.30396806560002 | 0.476602452496 | 9.555707841249701 | 9.469914267682038 | 0.5957067272727274 | 0.16373090909090848 | 0.10450963636363642 | 0.27710938842975197 | 0.13902876033057848 | 0.32113041322314045 | 0.35486650491798366 | 2.680781059173534e-2 | 1.0922264092859514e-2 | 0.15220772971530575 | 2.7787494356724177e-2 | 0.1724028184403606 | 0.390138090572179 | 0.16669581385483012 | 0.41521418381404146 |
| user_004 | Standing | 1.446046419322e12 | 1.83997 | -9.00468 | -1.41845 | 1.359228090909091 | -9.293821818181819 | -0.7147499090909091 | 3.3854896009 | 81.0842619024 | 2.0120004025 | 9.29955654350249 | 9.441341967736482 | 0.4807419090909091 | 0.28914181818181817 | 0.7037000909090909 | 0.3087787520661157 | 0.14979636363636362 | 0.33759865289256197 | 0.2311127831563719 | 8.360299102148759e-2 | 0.4951938179454628 | 0.17162487585933053 | 3.273886906085642e-2 | 0.1925969898494252 | 0.41427632790123375 | 0.18093885448088926 | 0.4388587356421485 |
| user_004 | Standing | 1.446046419451e12 | 1.83997 | -8.50651 | -0.805325 | 1.6135350000000002 | -9.220664545454547 | -0.8192599090909091 | 3.3854896009 | 72.36071238010001 | 0.6485483556249999 | 8.7404090485872 | 9.421110056105645 | 0.22643499999999994 | 0.7141545454545462 | 1.3934909090909109e-2 | 0.3901694462809917 | 0.20996909090909066 | 0.28439353719008265 | 5.127280922499997e-2 | 0.5100167147933894 | 1.9418169137190133e-4 | 0.301298780906686 | 7.874105652922614e-2 | 0.1744526572158077 | 0.5489068963919892 | 0.28060836860155497 | 0.4176753011800048 |
| user_004 | Standing | 1.44604642107e12 | 1.38013 | -9.38788 | -0.345482 | 1.1293065454545452 | -9.342591818181818 | -0.13994536363636365 | 1.9047588169000003 | 88.13229089439999 | 0.119357812324 | 9.495072802439378 | 9.418072771570202 | 0.25082345454545485 | 4.528818181818117e-2 | 0.20553663636363637 | 0.21693703305785128 | 0.12921231404958677 | 0.21440377685950415 | 6.291240535011586e-2 | 2.051019412396636e-3 | 4.2245308887677684e-2 | 5.991265460036592e-2 | 3.2445094302855e-2 | 7.60337220970661e-2 | 0.24477061629281796 | 0.18012521839779957 | 0.27574212971010814 |
| user_004 | Standing | 1.446046421459e12 | 1.45677 | -9.38788 | -0.345482 | 1.2721372727272728 | -9.332141818181817 | -0.32457972727272727 | 2.1221788328999995 | 88.13229089439999 | 0.119357812324 | 9.506515004964962 | 9.426230789766656 | 0.18463272727272706 | 5.573818181818169e-2 | 2.090227272727274e-2 | 0.17544919008264473 | 5.795454545454539e-2 | 0.14979758677685953 | 3.408924398016521e-2 | 3.1067449123966797e-3 | 4.3690500516528987e-4 | 4.6409602075376435e-2 | 5.155366086100664e-3 | 3.4963583735538706e-2 | 0.21542887939033717 | 7.180087803154404e-2 | 0.18698551744864816 |
| user_004 | Walking | 1.446046367239e12 | -2.49022 | -3.37159 | -1.41845 | -0.20493654545454543 | -9.098735454545453 | 0.19797045454545453 | 6.2011956484 | 11.3676191281 | 2.0120004025 | 4.425021489100364 | 9.447952530726882 | 2.2852834545454543 | 5.727145454545454 | 1.6164204545454546 | 1.8382258753541914 | 2.605906067099567 | 0.8803017542174996 | 5.222520467619206 | 32.800195057520654 | 2.612815085872934 | 4.52667831201796 | 11.455561595336873 | 1.0851159381124824 | 2.127599189701378 | 3.384606564334601 | 1.0416889833882677 |
| user_004 | Walking | 1.446046367435e12 | 1.91661 | -5.78577 | -7.31978 | 0.3559323636363636 | -9.499356363636362 | -1.7737836363636363 | 3.6733938920999996 | 33.475134492900004 | 53.579179248399996 | 9.525109323960539 | 10.518010767307546 | 1.5606776363636363 | 3.713586363636362 | 5.545996363636363 | 2.028855586373475 | 3.4606490533254615 | 2.3382468330053783 | 2.435714684645587 | 13.79072368018594 | 30.758075665467764 | 5.0896186853443295 | 16.256597771688835 | 10.290884887381942 | 2.2560183255781254 | 4.031947143960203 | 3.2079409108308 |
| user_004 | Walking | 1.446046367693e12 | 1.80165 | -6.24561 | -1.03525 | -0.5289194545454546 | -8.28704 | -1.4428365454545453 | 3.2459427224999997 | 39.0076442721 | 1.0717425625 | 6.5821979275239055 | 9.789411532907668 | 2.3305694545454547 | 2.041429999999999 | 0.4075865454545453 | 2.358440900826446 | 4.4812586776859495 | 2.8775063223140496 | 5.431553982460298 | 4.167436444899996 | 0.16612679203557013 | 7.056808578148045 | 25.7913773011737 | 13.164478352186688 | 2.6564654295036565 | 5.078521172661753 | 3.6282886258106157 |
| user_004 | Walking | 1.446046368987e12 | -0.152682 | -9.2346 | 0.152682 | -0.47666409090909095 | -8.530897272727273 | -1.5194752727272727 | 2.3311793124000002e-2 | 85.27783716 | 2.3311793124000002e-2 | 9.23712405168665 | 9.76657303180138 | 0.3239820909090909 | 0.7037027272727272 | 1.6721572727272727 | 2.0157751157024792 | 3.4272936363636366 | 2.873388876033058 | 0.10496439522982645 | 0.4951975283710743 | 2.7961099447347104 | 5.828280083975668 | 17.897908770771984 | 13.376540338645782 | 2.414183109040337 | 4.230592011855077 | 3.6573952942833214 |
| user_004 | Walking | 1.446046369959e12 | -2.33694 | -5.93905 | 2.56686 | -0.4209249090909091 | -8.266136363636361 | -1.481156181818182 | 5.461288563599999 | 35.2723149025 | 6.5887702596 | 6.879125941985653 | 9.35353616825607 | 1.9160150909090907 | 2.3270863636363615 | 4.048016181818182 | 2.167155512396694 | 3.179637768595042 | 2.7061742975206613 | 3.671113828591371 | 5.415330943822304 | 16.386435008261856 | 5.794231878774539 | 13.132691215146282 | 12.55247195021168 | 2.4071210768830342 | 3.6239055196219288 | 3.54294678907427 |
| user_004 | Walking | 1.446046371837e12 | 0.1922 | -14.10128 | -7.97122 | -0.5010486363636365 | -9.628250000000001 | -1.139758 | 3.694084e-2 | 198.84609763839998 | 63.5403482884 | 16.199487237773916 | 10.316775974009568 | 0.6932486363636365 | 4.473029999999998 | 6.831462 | 1.8621772231404956 | 2.9760007438016527 | 1.9210819669421488 | 0.4805936718200416 | 20.00799738089998 | 46.668873057444 | 4.533172438923539 | 12.205092426485121 | 7.436123220206778 | 2.1291248058588623 | 3.493578741990099 | 2.726925598582913 |
| user_004 | Walking | 1.446046373132e12 | -3.86975 | -12.83671 | -0.652044 | -0.9399907272727274 | -9.830303636363636 | -2.0594450909090907 | 14.974965062499999 | 164.7811236241 | 0.42516137793599995 | 13.423160956516018 | 10.83597894331644 | 2.9297592727272725 | 3.006406363636364 | 1.4074010909090906 | 1.865976297520661 | 3.1080623966942142 | 2.387260115702479 | 8.583489396131437 | 9.038479223313225 | 1.9807778306920982 | 4.837057946633808 | 14.082332477305709 | 9.694053998251828 | 2.199331249865242 | 3.75264339863325 | 3.113527581097015 |
| user_004 | Walking | 1.446046374362e12 | -3.67815 | -7.7401 | 3.56319 | -1.4416375454545456 | -9.105700909090908 | -1.5891492727272727 | 13.5287874225 | 59.90914801 | 12.696322976100001 | 9.2808544007866 | 10.259898144501834 | 2.2365124545454544 | 1.365600909090908 | 5.152339272727273 | 2.17982432231405 | 3.5286358677685943 | 2.2593143553719006 | 5.001987959336933 | 1.8648658429099145 | 26.546599981287805 | 7.185240938756582 | 15.047537842465964 | 10.801522905784468 | 2.680529973485949 | 3.8791156005545857 | 3.2865670395999027 |
| user_004 | Walking | 1.446046374751e12 | 2.0699 | -11.80206 | -1.03525 | -0.16313245454545458 | -9.88255727272727 | -1.3383258181818178 | 4.28448601 | 139.2886202436 | 1.0717425625 | 12.02683868753963 | 10.911064012777535 | 2.2330324545454547 | 1.9195027272727305 | 0.30307581818181784 | 2.393278776859504 | 2.4176638842975198 | 2.7235916363636363 | 4.9864339430532985 | 3.6844907200074504 | 9.18549515665783e-2 | 7.541399251908342 | 7.92809590244951 | 12.209342634414334 | 2.7461608204743477 | 2.8156874653358654 | 3.494186977597841 |
| user_004 | Walking | 1.44604637488e12 | -6.62882 | -15.59577 | -1.07357 | -1.0967554545454545 | -10.439943636363637 | 1.681954545454547e-2 | 43.9412545924 | 243.2280418929 | 1.1525525448999998 | 16.980042668680195 | 11.156214455696425 | 5.532064545454546 | 5.155826363636363 | 1.0903895454545454 | 2.5699958016528925 | 2.8414043801652884 | 1.8181555867768593 | 30.603738135075208 | 26.582545491967764 | 1.18894936083657 | 9.199901619889102 | 10.711808598846353 | 5.263144219387184 | 3.0331339600962406 | 3.272889946033376 | 2.2941543582303225 |
| user_004 | Walking | 1.44604637501e12 | 2.75966 | -8.04667 | -1.38013 | -0.6612972727272727 | -10.23789090909091 | 0.3756364545454545 | 7.615723315599999 | 64.7488980889 | 1.9047588169000003 | 8.617968450940163 | 10.94460809488589 | 3.4209572727272723 | 2.1912209090909087 | 1.7557664545454545 | 2.729293834710744 | 2.3508403305785124 | 1.8507755371900827 | 11.702948661825618 | 4.8014490724371885 | 3.0827158429071155 | 10.14018488805799 | 7.814975427667318 | 5.375853181447623 | 3.1843656963448765 | 2.795527754766051 | 2.3185886184158724 |
| user_004 | Walking | 1.446046375398e12 | -5.51753 | -15.02096 | 0.459245 | -1.013147181818182 | -10.004486363636364 | -2.010674090909091 | 30.4431373009 | 225.6292393216 | 0.210905970025 | 16.008850133364515 | 11.250955163400983 | 4.504382818181818 | 5.016473636363637 | 2.4699190909090913 | 2.485437545454545 | 3.9216566942148763 | 2.4718181157024794 | 20.289464572731575 | 25.165007744331408 | 6.100500315637192 | 8.475776219770623 | 18.712293954117953 | 10.82504804143981 | 2.911318639340363 | 4.325770908649457 | 3.290144076091473 |
| user_004 | Walking | 1.446046375463e12 | -4.138 | -1.91542 | 1.57053 | -1.5775017272727274 | -9.10570090909091 | -1.773785 | 17.123044 | 3.6688337763999996 | 2.4665644809 | 4.822700722344276 | 10.596033530201407 | 2.5604982727272727 | 7.19028090909091 | 3.344315 | 2.5152071652892563 | 4.400818347107438 | 2.7482944049586777 | 6.556151404639347 | 51.7001395516372 | 11.184442819225 | 8.618477807187535 | 23.07735293881157 | 11.833465120317847 | 2.9357244092706547 | 4.803889355388149 | 3.439980395339172 |
| user_004 | Walking | 1.446046375657e12 | -3.02671 | -7.28026 | 1.95374 | -0.8459320909090909 | -8.321876363636365 | -1.5020585454545454 | 9.1609734241 | 53.0021856676 | 3.8170999876000002 | 8.122823344090403 | 9.687379919583718 | 2.180777909090909 | 1.041616363636365 | 3.4557985454545452 | 2.102550933884298 | 4.184197603305785 | 3.2401248016528927 | 4.755792288778918 | 1.084964648995044 | 11.942543586765751 | 5.808805079582124 | 22.244903839339518 | 14.20277269512039 | 2.410146277631738 | 4.716450343143614 | 3.768656616769481 |
| user_004 | Walking | 1.446046377082e12 | 1.41845 | -13.25823 | 0.842448 | -0.6787151818181819 | -9.809402727272726 | -0.376834 | 2.0120004025 | 175.78066273289997 | 0.7097186327039999 | 13.360478351021118 | 10.527579477583433 | 2.097165181818182 | 3.4488272727272733 | 1.219282 | 1.7934540991735537 | 2.305236611570247 | 2.2209938099173554 | 4.398101799830488 | 11.894409557107442 | 1.4866485955239999 | 5.806652769430188 | 8.155345279416602 | 7.946394923938269 | 2.4096997259887356 | 2.855756516129588 | 2.8189350691242017 |
| user_004 | Walking | 1.446046377211e12 | -2.6435 | -8.77475 | 0.880768 | -0.9121201818181818 | -9.973134545454544 | 0.48711490909090904 | 6.98809225 | 76.99623756249999 | 0.775752269824 | 9.20652388702294 | 10.432936563834183 | 1.7313798181818183 | 1.1983845454545445 | 0.39365309090909095 | 1.9562359256198347 | 2.136437603305785 | 1.7009778595041318 | 2.9976760748073064 | 1.4361255187842954 | 0.15496275598228101 | 6.198894399521225 | 7.204444397644102 | 4.908080225721393 | 2.4897578997808654 | 2.684109609841614 | 2.2154187472623303 |
| user_004 | Walking | 1.446046377729e12 | -3.52487 | -6.93538 | 2.75846 | -1.5496309090909093 | -9.178857272727273 | -1.6100526363636367 | 12.424708516899999 | 48.0994957444 | 7.609101571599999 | 8.254290147123495 | 10.336100336423819 | 1.9752390909090907 | 2.2434772727272723 | 4.368512636363636 | 2.1152172148760333 | 3.44851173553719 | 2.6352338512396694 | 3.901569466255371 | 5.033190273243799 | 19.08390265406877 | 5.595292362165221 | 14.890526513806611 | 11.644638679226706 | 2.365437034073243 | 3.858824498964239 | 3.4124241646118243 |
| user_004 | Walking | 1.446046377988e12 | -0.229323 | -9.19628 | 0.152682 | -1.093271 | -8.489091818181818 | -1.5787000909090911 | 5.2589038329e-2 | 84.57156583839999 | 2.3311793124000002e-2 | 9.20040578832548 | 9.547832791509201 | 0.8639480000000002 | 0.7071881818181822 | 1.7313820909090911 | 1.630355173553719 | 3.065624545454545 | 3.021603834710744 | 0.7464061467040003 | 0.5001151245033063 | 2.997683944720736 | 3.620988301954705 | 13.128522741809842 | 12.93487508524121 | 1.9028894613073837 | 3.6233303384883144 | 3.5965087355991794 |
| user_004 | Walking | 1.446046378441e12 | -1.57053 | -5.86241 | 0.612526 | -0.5707226363636363 | -10.133382727272728 | -4.083000000000018e-3 | 2.4665644809 | 34.3678510081 | 0.375188100676 | 6.099967507263952 | 10.510226404961669 | 0.9998073636363637 | 4.270972727272729 | 0.6166090000000001 | 2.1231347355371906 | 2.47847 | 1.240184652892562 | 0.999614764381496 | 18.24120803710745 | 0.3802066588810001 | 7.28347342161239 | 8.464145353255374 | 2.2367967334880023 | 2.6987911037374475 | 2.909320428082024 | 1.495592435621417 |
| user_004 | Walking | 1.446046379995e12 | -4.98104 | -6.59049 | 3.21831 | -1.6471744545454547 | -8.83049 | -1.676241909090909 | 24.8107594816 | 43.4345584401 | 10.357519256099998 | 8.865824111598426 | 10.258849067062076 | 3.3338655454545454 | 2.2399999999999993 | 4.894551909090909 | 2.429696867768595 | 3.642013553719009 | 2.383144049586777 | 11.114659475168933 | 5.017599999999997 | 23.956638390785464 | 9.371850905559276 | 15.878911996568291 | 10.234845140065339 | 3.061347890318785 | 3.9848352533785247 | 3.199194451743335 |
| user_004 | Walking | 1.446046380074e12 | -0.804128 | -8.96635 | 0.229323 | -0.6578142727272728 | -8.398515454545453 | -1.885262 | 0.646621840384 | 80.3954323225 | 5.2589038329e-2 | 9.00525642062529 | 9.673506752782716 | 0.14631372727272718 | 0.5678345454545468 | 2.114585 | 2.1418190165289257 | 3.4998168595041323 | 2.54719279338843 | 2.140770678843799e-2 | 0.3224360710115718 | 4.471469722225 | 7.368126209589817 | 16.012725198798947 | 10.633785741520972 | 2.714429260376814 | 4.001590333704707 | 3.2609485953508948 |
| user_004 | Walking | 1.446046380082e12 | -1.99206 | -4.44456 | 1.95374 | -0.8250304545454547 | -8.119822727272727 | -1.6239869090909094 | 3.9683030435999997 | 19.7541135936 | 3.8170999876000002 | 5.247810650623744 | 9.462589951607004 | 1.1670295454545454 | 3.6752627272727265 | 3.5777269090909094 | 2.1741222314049584 | 3.6116102479338834 | 2.755262933884298 | 1.361957959963843 | 13.50755611448016 | 12.800129836033193 | 7.432045347332212 | 16.696989967439066 | 11.646397005756084 | 2.7261777908515454 | 4.08619504764996 | 3.4126817908729907 |
| user_004 | Walking | 1.446046380171e12 | 0.690364 | -9.8094 | -0.767005 | -1.7934879090909093 | -7.3255481818181805 | 1.2535205454545453 | 0.476602452496 | 96.22432836 | 0.5882966700250001 | 9.863530173448094 | 8.124101615562969 | 2.4838519090909092 | 2.4838518181818197 | 2.020525545454545 | 1.3906151570247933 | 2.361923801652893 | 2.43508226446281 | 6.1695203062945545 | 6.169519854685131 | 4.0825234798343875 | 2.519668729341154 | 6.412058420537043 | 7.494884325004188 | 1.5873464427594732 | 2.5322042612192726 | 2.73767863800779 |
| user_004 | Walking | 1.44604638022e12 | -0.842448 | -8.81307 | 0.689167 | 4.082909090909087e-3 | -9.530707272727271 | -0.42908963636363634 | 0.7097186327039999 | 77.6702028249 | 0.47495115388899994 | 8.880026610967615 | 9.590578855630675 | 0.8465309090909091 | 0.7176372727272717 | 1.1182566363636364 | 1.4267191570247935 | 1.7006610743801664 | 1.466306438016529 | 0.716614580046281 | 0.5150032552074366 | 1.250497904771314 | 2.718214575984966 | 3.7857442812020308 | 2.529815208583772 | 1.6487008752302421 | 1.9456989184357458 | 1.590539282313949 |
| user_004 | Walking | 1.446046380365e12 | -1.07237 | -11.15061 | -2.18486 | 2.5227720909090903 | -10.830114545454544 | -0.9481552727272727 | 1.1499774169 | 124.33610337210001 | 4.7736132196000005 | 11.41313690483909 | 11.350884475526653 | 3.59514209090909 | 0.3204954545454566 | 1.2367047272727274 | 2.564294082644628 | 0.8972012396694219 | 0.9469231818181818 | 12.925046653826184 | 0.10271733638429882 | 1.5294385824587111 | 7.856641240894614 | 0.9666613927906087 | 1.3448840650196614 | 2.802970074919569 | 0.9831893982293588 | 1.1596913662779686 |
| user_004 | Walking | 1.446046380398e12 | -1.14901 | -14.02463 | 0.229323 | 0.5510166363636363 | -11.5582 | -0.8959008181818181 | 1.3202239801000002 | 196.6902466369 | 5.2589038329e-2 | 14.073487828371793 | 11.88263241539135 | 1.7000266363636363 | 2.466430000000001 | 1.1252238181818182 | 2.447750256198347 | 0.912086033057852 | 0.6447948925619834 | 2.890090564345859 | 6.083276944900004 | 1.2661286410036694 | 7.293689833785208 | 1.2568161384298284 | 0.589618921873151 | 2.7006832161112877 | 1.1210781143300534 | 0.7678664739869497 |
| user_004 | Walking | 1.446046380437e12 | 1.87829 | -12.53014 | 1.80046 | -0.45924609090909113 | -12.65903909090909 | -0.17478218181818161 | 3.5279733241 | 157.0044084196 | 3.2416562115999996 | 12.797423098237395 | 12.823886745883119 | 2.337536090909091 | 0.12889909090909057 | 1.9752421818181816 | 2.2504479256198344 | 1.0511178512396695 | 1.3377275619834708 | 5.4640749763025545 | 1.6614975637189996e-2 | 3.90158167683385 | 6.601606710358293 | 1.8721697833812165 | 2.122333148807431 | 2.569359202283381 | 1.3682725544938832 | 1.4568229641268808 |
| user_004 | Walking | 1.446046380551e12 | -6.05401 | -8.50651 | 3.75479 | -6.193358272727273 | -13.34532 | 9.69440909090911e-2 | 36.6510370801 | 72.36071238010001 | 14.098447944099998 | 11.095503476827899 | 15.273407674630205 | 0.1393482727272728 | 4.838809999999999 | 3.657845909090909 | 4.122443181818182 | 1.642073057851239 | 2.524388297520661 | 1.94179411120744e-2 | 23.414082216099988 | 13.379836694653097 | 22.11843500412494 | 4.832016730455144 | 7.712770051733102 | 4.703024027593836 | 2.1981848717646892 | 2.77718743547012 |
| user_004 | Walking | 1.446046380607e12 | 2.8363 | -8.12331 | -1.45677 | -3.0023209999999994 | -8.910614545454546 | 0.8947030000000002 | 8.04459769 | 65.9881653561 | 2.1221788328999995 | 8.726679888651812 | 10.40415012803502 | 5.838621 | 0.7873045454545462 | 2.3514730000000004 | 4.679512611570247 | 3.070059256198347 | 2.48100147107438 | 34.089495181641 | 0.6198484472933896 | 5.529425269729002 | 26.36158899282286 | 12.161785970066035 | 7.33708094032403 | 5.13435380479597 | 3.487375226451268 | 2.708704660963249 |
| user_004 | Walking | 1.446046380632e12 | 1.91661 | -9.04299 | -1.95493 | 6.33044545454546e-2 | -8.078019090909093 | -0.29670972727272726 | 3.6733938920999996 | 81.7756681401 | 3.8217513049000003 | 9.448323308243637 | 8.76231745154077 | 1.8533055454545453 | 0.9649709090909067 | 1.6582202727272728 | 4.297892743801652 | 2.7109246280991726 | 1.9780874628099172 | 3.4347414448125697 | 0.9311688553917309 | 2.749694472883711 | 23.239252805113832 | 10.62169050586551 | 4.62037458539075 | 4.820710819486462 | 3.2590935098375913 | 2.149505660702188 |
| user_004 | Walking | 1.446046380712e12 | -2.79678 | -4.59784 | 0.497565 | -0.2920294545454546 | -7.078208181818183 | -0.1991674545454547 | 7.8219783684 | 21.140132665599996 | 0.24757092922499999 | 5.404598224033402 | 7.330404214633282 | 2.5047505454545456 | 2.480368181818183 | 0.6967324545454547 | 1.6601237107438016 | 1.2601368595041322 | 1.0260964876033059 | 6.273775294954843 | 6.152226317376039 | 0.48543611321693414 | 3.3548485403621124 | 2.1182480306740796 | 1.2529168440708105 | 1.8316245631575572 | 1.4554202247715535 | 1.1193376809840767 |
| user_004 | Walking | 1.446046380907e12 | 0.805325 | -13.90968 | -7.08986 | 2.522717363636364 | -13.554396363636364 | -7.925881818181819 | 0.6485483556249999 | 193.4791977024 | 50.2661148196 | 15.633101447813386 | 16.06838302763538 | 1.717392363636364 | 0.3552836363636356 | 0.8360218181818189 | 1.7950017685950412 | 5.5650118181818184 | 1.963494917355372 | 2.949436530676497 | 0.12622646226776804 | 0.6989324804760342 | 3.9698473877730645 | 38.85068078559317 | 5.016328901239086 | 1.992447587208523 | 6.233031428253284 | 2.2397162546267073 |
| user_004 | Walking | 1.446046381198e12 | -0.727487 | -5.59417 | 1.07237 | 1.7598499090909088 | -12.40124818181818 | -0.16084736363636362 | 0.529237335169 | 31.2947379889 | 1.1499774169 | 5.7422950760971 | 13.05166984113652 | 2.4873369090909088 | 6.807078181818181 | 1.2332173636363637 | 2.9763167438016533 | 6.872004049586777 | 2.498737305785124 | 6.186844899325916 | 46.33631337338511 | 1.5208250659742233 | 12.050094489829663 | 55.48691985462485 | 8.704269508248906 | 3.471324601622508 | 7.448954279267987 | 2.9502999014081444 |
| user_004 | Walking | 1.446046381207e12 | -2.14534 | -5.24928 | 1.91542 | 1.7737844545454542 | -11.662710909090912 | -0.3141282727272728 | 4.6024837156 | 27.5549405184 | 3.6688337763999996 | 5.985503989673718 | 12.319523421340348 | 3.919124454545454 | 6.413430909090912 | 2.2295482727272726 | 3.257860371900826 | 6.713022644628097 | 2.518372826446281 | 15.359536490216204 | 41.13209602568268 | 4.970885500421165 | 13.384968565883804 | 53.1696613832105 | 8.78758510020929 | 3.6585473300046023 | 7.291752970528452 | 2.9643861253570343 |
| user_004 | Walking | 1.446046381312e12 | 0.268841 | -10.15428 | -1.41845 | -1.8283258181818183 | -8.03970090909091 | 0.5846565454545453 | 7.2275483281e-2 | 103.1094023184 | 2.0120004025 | 10.256396940650308 | 8.585679636059387 | 2.0971668181818184 | 2.114579090909089 | 2.0031065454545454 | 2.033827181818182 | 1.8767451239669422 | 1.6579069421487602 | 4.398108663282852 | 4.4714447317099095 | 4.012435832442843 | 5.23885645013378 | 4.130406108873178 | 3.3989535320892843 | 2.288854833783432 | 2.0323400573902926 | 1.8436251061670006 |
| user_004 | Walking | 1.446046381368e12 | -1.34061 | -8.77475 | 0.344284 | -0.598591 | -9.471485454545455 | -0.1608472727272727 | 1.7972351721000002 | 76.99623756249999 | 0.11853147265599999 | 8.883242888003005 | 9.552652784403914 | 0.7420190000000001 | 0.6967354545454558 | 0.5051312727272727 | 1.2291001239669423 | 1.2876880165289257 | 1.3139746198347106 | 0.5505921963610001 | 0.48544029362066293 | 0.2551576026870743 | 1.9203610615438131 | 2.456178409308113 | 2.234292934221153 | 1.3857709267926692 | 1.56721996200537 | 1.49475514189487 |
| user_004 | Walking | 1.446046381433e12 | 5.21216 | -10.65245 | 1.57053 | 0.6415934545454545 | -9.499355454545453 | 5.8623090909090896e-2 | 27.1666118656 | 113.4746910025 | 2.4665644809 | 11.962770053336309 | 9.817063986868506 | 4.570566545454545 | 1.1530945454545467 | 1.5119069090909092 | 1.6252866528925622 | 0.5504190082644638 | 0.9212699834710744 | 20.890078546428292 | 1.3296270307570277 | 2.2858625017568266 | 4.794645449754335 | 0.436512278266267 | 1.0913816417132172 | 2.189667885720192 | 0.6606907584235359 | 1.0446921277166863 |
| user_004 | Walking | 1.446046381627e12 | -11.07397 | -13.64143 | 1.41725 | -5.562814181818182 | -14.87813272727273 | -0.7809399090909093 | 122.63281156089998 | 186.08861244489998 | 2.0085975625 | 17.627535890427225 | 16.445116127399935 | 5.511155818181817 | 1.2367027272727302 | 2.1981899090909094 | 3.8383654462809917 | 0.9557900826446282 | 1.4979756033057854 | 30.372838452279293 | 1.529433635643809 | 4.832038876429101 | 21.521791730657057 | 1.2418094264423745 | 2.890757771440496 | 4.639158515362141 | 1.1143650328516121 | 1.7002228593453554 |
| user_004 | Walking | 1.446046381708e12 | 3.41111 | -8.62147 | -2.49142 | -3.5527397272727272 | -9.22762909090909 | 0.16313245454545455 | 11.635671432099999 | 74.3297449609 | 6.207173616400001 | 9.600655707262916 | 10.955537007966514 | 6.963849727272727 | 0.6061590909090899 | 2.654552454545455 | 5.124472049586776 | 3.176785041322314 | 1.944833743801653 | 48.495203024036435 | 0.3674288434917343 | 7.046648733933299 | 31.53765594406279 | 12.860114444126896 | 4.145003702053794 | 5.615839736322858 | 3.586100172070894 | 2.035928216331262 |
| user_004 | Walking | 1.446046381862e12 | -2.87342 | -1.8771 | -1.95493 | -2.542477 | -3.479583636363636 | 0.19797027272727277 | 8.2565424964 | 3.52350441 | 3.8217513049000003 | 3.949911164988398 | 4.69983628260057 | 0.330943 | 1.6024836363636359 | 2.152900272727273 | 2.015458446280992 | 2.2865500826446286 | 1.045415347107438 | 0.109523269249 | 2.5679538048132216 | 4.634979584309167 | 4.580855063376293 | 5.364486133914651 | 1.4248327615049055 | 2.1402932190184347 | 2.3161360352782934 | 1.1936635880786954 |
| user_004 | Walking | 1.44604638191e12 | 0.460442 | -5.01936 | -5.51872 | -1.9920577272727273 | -2.6922745454545454 | -2.6899873636363636 | 0.21200683536400003 | 25.193974809599997 | 30.4562704384 | 7.474105436998062 | 4.892939744498533 | 2.4524997272727274 | 2.3270854545454545 | 2.8287326363636365 | 1.5277448429752065 | 1.5746160330578511 | 2.156387975206611 | 6.014754912272802 | 5.415326712757024 | 8.001728328028769 | 2.8772256045321325 | 2.9820860129181073 | 5.597216075395045 | 1.696238663788835 | 1.7268717418841815 | 2.3658436286861915 |
| user_004 | Walking | 1.446046381968e12 | 3.41111 | -17.09026 | -7.81794 | 1.5717320000000001 | -8.436836363636363 | -5.766062727272727 | 11.635671432099999 | 292.0769868676 | 61.120185843600005 | 19.100598004861 | 10.599165234665834 | 1.8393779999999997 | 8.653423636363637 | 2.051877272727273 | 2.4604179834710744 | 4.235818429752066 | 2.2330285702479333 | 3.383311426883999 | 74.88174063037688 | 4.210200342334711 | 6.282738465958101 | 25.915239859435236 | 5.413362502127287 | 2.5065391411183073 | 5.0907013131232945 | 2.3266633839314372 |
| user_004 | Walking | 1.446046382032e12 | 1.76333 | -8.42987 | -4.48408 | 2.6586345454545453 | -14.299844545454544 | -7.828394545454546 | 3.1093326889000004 | 71.06270821689999 | 20.106973446399998 | 9.709738119650806 | 16.551329933455662 | 0.8953045454545452 | 5.869974545454545 | 3.344314545454546 | 1.1743123305785124 | 5.242281074380166 | 2.570311074380166 | 0.8015702291115697 | 34.45660116428429 | 11.184439778938849 | 2.279430665595875 | 35.05983585959181 | 7.693321614627501 | 1.5097783498235344 | 5.921134676697686 | 2.773683762548914 |
| user_004 | Walking | 1.44604638204e12 | 1.57173 | -6.01569 | -5.2888 | 2.4600654545454543 | -13.64839909090909 | -7.682080909090908 | 2.4703351929000004 | 36.188526176100005 | 27.97140544 | 8.16273647798335 | 15.898460266477004 | 0.8883354545454543 | 7.632709090909089 | 2.393280909090908 | 0.9633922644628098 | 5.271417024793389 | 2.6086320661157028 | 0.7891398798024788 | 58.25824806644626 | 5.727793509819004 | 1.4153351027918004 | 35.49527038710307 | 7.860593928020138 | 1.1896785712081228 | 5.957790730388495 | 2.803675075328833 |
| user_004 | Walking | 1.446046382072e12 | -1.26397 | -6.13065 | -6.63001 | 1.4950900909090912 | -8.994222727272728 | -7.093341818181818 | 1.5976201609 | 37.5848694225 | 43.95703260010001 | 9.11808763850732 | 11.953554612570894 | 2.759060090909091 | 2.863572727272728 | 0.46333181818181757 | 0.9143041239669419 | 4.723848512396693 | 1.769703140495868 | 7.612412585247283 | 8.200048764380169 | 0.21467637373966886 | 1.320082722573171 | 29.308365139374224 | 4.4012970859469585 | 1.1489485291226804 | 5.413720083212119 | 2.0979268542890046 |
| user_004 | Walking | 1.446046382316e12 | -0.229323 | -4.82776 | -0.575403 | 1.6448873636363635 | -12.763548181818182 | 0.10739427272727241 | 5.2589038329e-2 | 23.307266617599996 | 0.331088612409 | 4.867334410982874 | 13.864333837520315 | 1.8742103636363634 | 7.935788181818182 | 0.6827972727272724 | 3.4006886776859506 | 7.745771223140497 | 3.2724279338842974 | 3.5126644871619495 | 62.97673406668513 | 0.4662121156438012 | 17.78203322292181 | 69.75729677875341 | 15.412142378741247 | 4.216874817079802 | 8.35208337953791 | 3.9258301515400853 |
| user_004 | Walking | 1.446046382348e12 | -5.2876 | -4.21464 | 3.56319 | 0.8610633636363635 | -8.966354545454545 | -0.6137239090909095 | 27.958713760000002 | 17.7631903296 | 12.696322976100001 | 7.6431817370582 | 10.495678238910019 | 6.148663363636364 | 4.751714545454544 | 4.17691390909091 | 4.76945173553719 | 6.548972297520662 | 3.775976297520661 | 37.806061159324045 | 22.578791121484286 | 17.446609803957106 | 27.11017661326442 | 48.57075699291013 | 17.554459215252283 | 5.206743378856348 | 6.969272343143875 | 4.189804197722404 |
| user_004 | Walking | 1.446046382364e12 | -4.82776 | -5.51753 | 1.83878 | -0.8424475454545456 | -6.322253636363637 | -8.420663636363647e-2 | 23.307266617599996 | 30.4431373009 | 3.3811118884000004 | 7.558539264097263 | 8.21948649895524 | 3.985312454545454 | 0.8047236363636374 | 1.9229866363636365 | 4.404617619834711 | 5.154555454545456 | 3.5530224628099174 | 15.882715360355112 | 0.6475801309223157 | 3.697877603633133 | 22.99147869562731 | 32.18715296810331 | 15.914985124369508 | 4.79494303361649 | 5.673372274767742 | 3.9893589866505508 |
| user_004 | Walking | 1.446046382444e12 | 1.80165 | -9.77108 | -1.26517 | -0.9365071818181817 | -8.966354545454546 | 0.40002172727272717 | 3.2459427224999997 | 95.4740043664 | 1.6006551288999997 | 10.01601728322191 | 9.432739406241023 | 2.7381571818181816 | 0.8047254545454532 | 1.665191727272727 | 2.62224879338843 | 2.3023836363636363 | 1.404233611570248 | 7.497504752342486 | 0.6475830571933863 | 2.7728634885775283 | 7.855936617997744 | 6.33018332636108 | 2.3984448982685023 | 2.8028443799108334 | 2.5159855576614665 | 1.5486913502271853 |
| user_004 | Walking | 1.446046382526e12 | 1.49509 | -9.77108 | -0.537083 | 0.18871663636363636 | -9.586446363636364 | 0.35473372727272723 | 2.2352941081 | 95.4740043664 | 0.28845814888899995 | 9.899381628333611 | 9.712099484579783 | 1.3063733636363637 | 0.1846336363636354 | 0.8918167272727272 | 1.3019397933884298 | 0.37655123966942144 | 0.9212700578512397 | 1.706611365218587 | 3.408957967685915e-2 | 0.7953370750434379 | 2.2620555639238655 | 0.17967579373283246 | 1.0729043552679676 | 1.5040131528427088 | 0.4238818157609883 | 1.0358109650259393 |
| user_004 | Walking | 1.446046382542e12 | 3.06622 | -9.92436 | 0.727487 | 0.3419984545454545 | -9.628250000000001 | 0.5498190909090909 | 9.4017050884 | 98.4929214096 | 0.529237335169 | 10.412678033684179 | 9.782887078405635 | 2.7242215454545455 | 0.29610999999999876 | 0.17766790909090913 | 1.2880055454545454 | 0.33443033057851257 | 0.7949080743801654 | 7.421383028718752 | 8.768113209999927e-2 | 3.156588592073555e-2 | 2.203681134481383 | 0.1306830720105938 | 0.8324067890313998 | 1.4844800889474346 | 0.3615011369423252 | 0.9123632988187325 |
| user_004 | Walking | 1.44604638255e12 | 3.44943 | -10.15428 | 1.11069 | 0.5614693636363636 | -9.711857272727274 | 0.5951067272727273 | 11.8985673249 | 103.1094023184 | 1.2336322760999998 | 10.781539867727615 | 9.916425850711208 | 2.8879606363636365 | 0.44242272727272614 | 0.5155832727272727 | 1.4675725537190083 | 0.34519809917355376 | 0.798075049586777 | 8.340316637185861 | 0.195737869607437 | 0.2658261111161652 | 2.8861586223685416 | 0.13893548986183332 | 0.8355621408631894 | 1.6988698073626896 | 0.37274051277240217 | 0.9140908821682827 |
| user_004 | Walking | 1.446046382599e12 | 0.422122 | -11.03565 | -1.53341 | 2.1674385454545453 | -10.547936363636364 | -0.2583901818181818 | 0.178186982884 | 121.78557092250001 | 2.3513462280999997 | 11.149668341860398 | 10.854335944055732 | 1.7453165454545454 | 0.48771363636363674 | 1.2750198181818182 | 1.5822173305785123 | 0.5909556198347105 | 0.7198518347107438 | 3.046129843837388 | 0.23786459109504168 | 1.6256755367563966 | 3.3174814332004483 | 0.4400590329217879 | 0.6180679803025185 | 1.8213954631546792 | 0.6633694543177187 | 0.7861729964216009 |
| user_004 | Walking | 1.446046382687e12 | 2.79798 | -11.41886 | 1.72382 | 0.2549070909090908 | -12.028497272727272 | -0.1469123636363636 | 7.8286920804 | 130.39036369960002 | 2.9715553923999996 | 11.88236555456867 | 12.250013800084162 | 2.543072909090909 | 0.6096372727272712 | 1.8707323636363635 | 2.0300252066115703 | 0.8880161983471073 | 1.666774603305785 | 6.4672198209521 | 0.3716576042983452 | 3.4996395763564956 | 4.895253277219688 | 1.4451457322972197 | 3.0411812288386826 | 2.2125219269466436 | 1.20214214313334 | 1.7438982851183387 |
| user_004 | Walking | 1.446046382744e12 | -5.97737 | -15.67241 | -1.15021 | 0.4290891818181816 | -13.857420000000001 | 0.3651851818181818 | 35.7289521169 | 245.62443520809998 | 1.3229830441 | 16.812982197370577 | 14.241890340397338 | 6.406459181818181 | 1.814989999999998 | 1.5153951818181817 | 2.7090246694214875 | 1.3513440495867766 | 1.5945680743801653 | 41.042719248302475 | 3.294188700099993 | 2.2964225570777597 | 10.086290074801907 | 2.7503802530840717 | 3.074455089508204 | 3.1758920124591623 | 1.6584270418333367 | 1.7534124128419428 |
| user_004 | Walking | 1.446046382874e12 | 1.34181 | -8.46819 | -0.881966 | -0.7936763636363637 | -8.287039090909092 | -0.1712992727272726 | 1.8004540760999999 | 71.7102418761 | 0.7778640251560001 | 8.619081156211259 | 9.0857188458721 | 2.135486363636364 | 0.18115090909090803 | 0.7106667272727274 | 4.147777768595042 | 3.0995113223140494 | 1.454587743801653 | 4.56030200927686 | 3.2815651864462426e-2 | 0.5050471972525291 | 22.151839089038155 | 13.248915653020816 | 3.235745610163372 | 4.706574028849238 | 3.639905995080205 | 1.7988178368482373 |
| user_004 | Walking | 1.446046383109e12 | 3.25783 | -15.05928 | -6.89825 | 0.4116725454545454 | -7.186202727272726 | -5.65806909090909 | 10.613456308899998 | 226.78191411839998 | 47.5858530625 | 16.881386894737055 | 9.385308156033904 | 2.8461574545454544 | 7.873077272727273 | 1.2401809090909097 | 1.822907561983471 | 2.95668132231405 | 2.6529682479338845 | 8.100612256064661 | 61.985345742334715 | 1.5380486872735553 | 3.784964526944364 | 15.164207426378965 | 7.807534354208834 | 1.9454985291550246 | 3.894124731743831 | 2.7941965489580065 |
| user_004 | Walking | 1.446046383238e12 | -2.60518 | -17.89499 | 2.22198 | -1.7795530909090909 | -10.203056363636364 | -5.9890181818181825 | 6.7869628323999995 | 320.2306671001 | 4.937195120399999 | 18.21962746745663 | 12.910209327806722 | 0.825626909090909 | 7.691933636363636 | 8.210998181818182 | 2.0654962975206614 | 5.344258347107439 | 2.0062738842975207 | 0.681659793015008 | 59.16584306622231 | 67.42049114182149 | 4.659550859556643 | 34.04221112690023 | 9.778387378413225 | 2.1585992818391846 | 5.834570346383718 | 3.12704131383217 |
| user_005 | Jogging | 1.446046533265e12 | -2.68182 | -12.95167 | -2.49142 | -1.756662142857143 | -10.679820571428573 | -3.099071 | 7.192158512400001 | 167.7457557889 | 6.207173616400001 | 13.459015116928134 | 13.466680230426723 | 0.9251578571428571 | 2.271849428571427 | 0.6076509999999997 | 2.4322976343537417 | 4.564965994557824 | 2.023260533333333 | 0.8559170606331633 | 5.1612998261003185 | 0.36923973780099967 | 13.150513999738278 | 36.98679497306271 | 8.17024775704289 | 3.6263637434402907 | 6.0816769869060545 | 2.8583645248713276 |
| user_005 | Jogging | 1.4460465343e12 | -3.14167 | -17.74171 | 3.21831 | 0.5684354545454545 | -8.31839309090909 | -0.5754035454545446 | 9.8700903889 | 314.7682737241 | 10.357519256099998 | 18.30289275959131 | 13.293004784553316 | 3.7101054545454546 | 9.42331690909091 | 3.7937135454545445 | 4.222201925619835 | 7.810693338842976 | 4.760586694214876 | 13.764882483847934 | 88.79890156915867 | 14.39226246496529 | 25.92933548225511 | 79.23515047717568 | 32.34629293348333 | 5.092085572950941 | 8.901412836015172 | 5.687380146735695 |
| user_005 | Jogging | 1.446046535336e12 | 13.79591 | -19.58108 | -3.33447 | 0.8018412727272726 | -8.168594818181818 | -0.40818800000000005 | 190.32713272809997 | 383.4186939664 | 11.1186901809 | 24.183972313815612 | 13.264153394433299 | 12.994068727272726 | 11.412485181818182 | 2.926282 | 4.401135628099173 | 7.751153892561984 | 4.965488429752067 | 168.84582208908705 | 130.2448180252196 | 8.563126343524 | 30.875727116802615 | 81.80166573789738 | 34.38620489012605 | 5.5565931214011535 | 9.044427330566451 | 5.863975178164217 |
| user_005 | Jogging | 1.446046535466e12 | 2.60638 | 11.61165 | 7.58682 | 0.7356521818181817 | -8.203432090909091 | -0.5266325454545453 | 6.793216704400001 | 134.8304157225 | 57.559837712400004 | 14.113237408167553 | 13.255970167815521 | 1.8707278181818183 | 19.81508209090909 | 8.113452545454546 | 4.314676371900827 | 7.832861909090908 | 4.815374297520662 | 3.4996225697193064 | 392.63747826946616 | 65.82811220734285 | 30.481313367488237 | 83.02335248056642 | 32.21978126860708 | 5.520988441165969 | 9.111715122882542 | 5.676247111305768 |
| user_005 | Jogging | 1.446046535855e12 | -1.72382 | -1.03405 | -6.36177 | 1.2233646363636366 | -8.213882727272726 | -0.599789818181818 | 2.9715553923999996 | 1.0692594024999997 | 40.4721175329 | 6.671801280598816 | 13.037914555049912 | 2.9471846363636365 | 7.179832727272726 | 5.761980181818182 | 3.9606093884297526 | 8.004827487603306 | 4.7821222314049585 | 8.68589728081786 | 51.54999799161651 | 33.20041561566549 | 27.13730258213853 | 83.43492617701699 | 30.17409682118895 | 5.2093476157901515 | 9.134272066071658 | 5.493095377033695 |
| user_005 | Jogging | 1.446046536437e12 | 2.49142 | -13.67975 | 6.62882 | 1.3627123636363636 | -7.778423909090909 | -0.9481554545454542 | 6.207173616400001 | 187.13556006250002 | 43.9412545924 | 15.40402506721214 | 13.505095304889357 | 1.1287076363636366 | 5.901326090909091 | 7.576975454545455 | 4.147145239669422 | 7.830643504132232 | 5.837039223140496 | 1.2739809283855872 | 34.825649631244374 | 57.4105570387843 | 31.226787615125485 | 80.20664756182254 | 42.00361180619343 | 5.588093379241751 | 8.955816409564376 | 6.481019349314846 |
| user_005 | Jogging | 1.446046536566e12 | 2.72134 | -6.85874 | -8.54603 | 1.4358696363636363 | -8.659791181818182 | -0.5997890909090908 | 7.405691395600001 | 47.0423143876 | 73.0346287609 | 11.290820809139609 | 14.161374394521529 | 1.2854703636363638 | 1.801051181818182 | 7.946240909090909 | 4.340646545454546 | 7.698897280991735 | 5.504824561983471 | 1.6524340557874053 | 3.2437853595286703 | 63.14274458530991 | 33.926766159153416 | 82.07243264506836 | 39.69246186369529 | 5.824668759608001 | 9.059383679095856 | 6.30019538297784 |
| user_005 | Jogging | 1.446046537191e12 | -8.88971 | -19.58108 | 8.58315 | -3.7200084545454546 | -12.683423636363635 | 8.053690000000001 | 79.02694388409998 | 383.4186939664 | 73.67046392249999 | 23.154181086209892 | 19.006897586499694 | 5.1697015454545445 | 6.897656363636365 | 0.5294599999999985 | 3.929191355371901 | 8.388347768595041 | 4.496189636363636 | 26.725814069075106 | 47.57766331081324 | 0.2803278915999984 | 37.982441675489866 | 95.07989014595297 | 45.417514090548984 | 6.1629896702404 | 9.75089176157509 | 6.739251745598245 |
| user_005 | Jogging | 1.446046537199e12 | -7.39522 | -19.58108 | 2.6435 | -2.8212230000000003 | -15.661957272727271 | 6.855309090909091 | 54.6892788484 | 383.4186939664 | 6.98809225 | 21.097299947263394 | 18.48057756225118 | 4.573997 | 3.919122727272729 | 4.211809090909091 | 2.83911620661157 | 7.010399669421488 | 3.3991572892561988 | 20.921448556009004 | 15.359522951425634 | 17.73933581826446 | 14.939538625687815 | 63.39304411683022 | 22.938252788811482 | 3.865169934904262 | 7.961974887980382 | 4.789389605034391 |
| user_005 | Jogging | 1.446046537247e12 | 2.33814 | -11.8787 | -8.46939 | -3.695568181818182 | -17.215671818181818 | -4.0451345454545455 | 5.466898659600001 | 141.10351369 | 71.73056697210001 | 14.775011990577198 | 20.703948053627844 | 6.033708181818183 | 5.3369718181818175 | 4.424255454545455 | 4.618996033057852 | 5.3781446280991725 | 7.32362917355372 | 36.40563442333968 | 28.483268188066937 | 19.574036327075213 | 25.153184588790236 | 36.76646394369556 | 94.20583036550964 | 5.015295064977757 | 6.063535597627474 | 9.705968800975493 |
| user_005 | Jogging | 1.446046537506e12 | 4.25415 | -19.58108 | -7.12818 | 11.86596181818182 | -19.25013181818181 | -3.574839363636364 | 18.0977922225 | 383.4186939664 | 50.81095011240001 | 21.267990885396298 | 24.067887578832163 | 7.6118118181818195 | 0.3309481818181901 | 3.5533406363636364 | 8.285105297520662 | 6.445729578512396 | 4.262738148760331 | 57.93967915541241 | 0.1095266990487658 | 12.626229678033132 | 94.68215108093536 | 61.12387683692416 | 22.080502893756435 | 9.730475377952269 | 7.818176055636261 | 4.698989560932907 |
| user_005 | Jogging | 1.446046537628e12 | 1.95493 | 13.60431 | 7.7401 | 1.090985181818182 | 2.644700909090909 | 4.172831818181819 | 3.8217513049000003 | 185.0772505761 | 59.90914801 | 15.773653663340019 | 11.77319875359906 | 0.8639448181818181 | 10.95960909090909 | 3.5672681818181813 | 3.8969519008264473 | 13.113463801652891 | 4.991775173553719 | 0.7464006488632148 | 120.11303142553717 | 12.725402281012393 | 23.85319022932108 | 201.51069996120503 | 25.8405193778445 | 4.88397279162375 | 14.195446451633885 | 5.0833570972187765 |
| user_005 | Jogging | 1.4460465377e12 | -3.14167 | -3.90807 | -1.38013 | 0.41863736363636356 | 5.396795727272727 | 3.998649181818182 | 9.8700903889 | 15.2730111249 | 1.9047588169000003 | 5.200755746110366 | 8.354313194227272 | 3.5603073636363636 | 9.304865727272727 | 5.3787791818181825 | 1.4473028016528926 | 7.501913107438015 | 3.2230250247933885 | 12.675788523563314 | 86.58052620257462 | 28.931265486760676 | 3.4301140495762428 | 69.1345025313832 | 12.806767356329642 | 1.8520567079806824 | 8.314716022293437 | 3.5786544058248544 |
| user_005 | Jogging | 1.446046537798e12 | 12.60798 | -11.95534 | 0.229323 | 7.236171454545453 | -15.864010909090911 | -2.282398818181818 | 158.9611596804 | 142.93015451559998 | 5.2589038329e-2 | 17.376533118960438 | 23.27245020551651 | 5.371808545454546 | 3.9086709090909117 | 2.511721818181818 | 6.8476180165289255 | 8.136574719008266 | 12.404698256198346 | 28.856327049018486 | 15.277708275573575 | 6.308746491930577 | 72.38351563915937 | 83.04199427584591 | 203.43031800479287 | 8.507850236056072 | 9.112738023000876 | 14.262900055907034 |
| user_005 | Jogging | 1.446046537806e12 | 9.77228 | -10.57581 | 5.13432 | 8.497257818181817 | -16.17057363636364 | -0.5788870000000004 | 95.4974563984 | 111.84775715610002 | 26.361241862399996 | 15.287460724950368 | 23.214003915053574 | 1.2750221818181835 | 5.594763636363638 | 5.713207000000001 | 6.64493258677686 | 7.828745190082646 | 11.612955909090909 | 1.625681564128401 | 31.301380146776875 | 32.640734224849005 | 71.41476358365733 | 78.55517815891763 | 187.48812677917655 | 8.45072562468202 | 8.863135909987934 | 13.692630382040427 |
| user_005 | Jogging | 1.446046537927e12 | -3.83143 | -18.50811 | -14.17911 | -4.218119 | -18.00646363636363 | 1.1385608181818176 | 14.6798558449 | 342.55013577209996 | 201.04716039209998 | 23.62788928383363 | 20.373295370199216 | 0.3866889999999996 | 0.5016463636363682 | 15.317670818181817 | 6.313667504132232 | 4.587668512396696 | 4.9331842727272734 | 0.1495283827209997 | 0.2516490741495913 | 234.6310392941788 | 50.65188862554589 | 26.09155367340835 | 49.933594930582096 | 7.11701402454329 | 5.107989200596292 | 7.066370704299492 |
| user_005 | Jogging | 1.446046538089e12 | 0.805325 | -0.650847 | -0.728685 | -6.789063272727273 | -8.649263636363637e-2 | -4.107839545454545 | 0.6485483556249999 | 0.42360181740899994 | 0.530981829225 | 1.2661484913938805 | 9.135670010436984 | 7.594388272727273 | 0.5643543636363636 | 3.379154545454545 | 7.67958094214876 | 3.2204892809917354 | 3.0206535537190082 | 57.674733236937534 | 0.3184958477554049 | 11.418685442066112 | 73.21571867055573 | 14.941258901488242 | 12.832239867491966 | 8.556618413284289 | 3.8653924640957538 | 3.5822115888780166 |
| user_005 | Jogging | 1.446046538178e12 | 15.86521 | -19.58108 | -6.13185 | 3.5330351818181813 | -11.899599272727274 | -0.930738 | 251.70488834409997 | 383.4186939664 | 37.5995844225 | 25.936907424228508 | 13.572404537765589 | 12.332174818181818 | 7.681480727272726 | 5.201112 | 4.570884867768594 | 7.491462446280992 | 4.1911655206611576 | 152.08253574619775 | 59.00514616346233 | 27.051566036544003 | 38.07839845566661 | 76.72006506801198 | 21.170354081173457 | 6.1707696809771315 | 8.758999090536086 | 4.601125305963038 |
| user_005 | Jogging | 1.446046538493e12 | 9.77228 | -12.83671 | 11.61046 | 1.1467243636363635 | -8.997707272727272 | -2.7074071818181817 | 95.4974563984 | 164.7811236241 | 134.8027814116 | 19.876653677973565 | 13.231876040647904 | 8.625555636363636 | 3.839002727272728 | 14.317867181818182 | 2.7254921900826443 | 6.384607595041322 | 8.92989958677686 | 74.40021003600448 | 14.737941940007444 | 205.0013206361861 | 13.09131786918851 | 45.14691348141067 | 102.19151263494622 | 3.618192624666148 | 6.719145293964901 | 10.108981780325168 |
| user_005 | Jogging | 1.44604653851e12 | 19.54396 | -19.58108 | -17.24474 | 4.961337090909091 | -11.714966363636364 | -3.1637672727272728 | 381.96637248159993 | 383.4186939664 | 297.38105766760003 | 32.60009392801806 | 17.782204210454342 | 14.582622909090908 | 7.866113636363636 | 14.080972727272727 | 4.728282619834711 | 6.480883942148759 | 10.929839975206612 | 212.652890908743 | 61.875743740185946 | 198.2737929461983 | 47.499936104067004 | 46.55667068685511 | 135.73274442981278 | 6.892019740545365 | 6.823244879590289 | 11.650439666802828 |
| user_005 | Jogging | 1.446046538793e12 | -0.574206 | -3.98471 | -7.28146 | -7.8376469090909096 | -0.5324014545454545 | -6.713621181818181 | 0.329712530436 | 15.877913784100002 | 53.0196597316 | 8.320293627398975 | 12.435762186053363 | 7.263440909090909 | 3.452308545454546 | 0.5678388181818192 | 7.900635925619834 | 5.027245347107438 | 2.382826198347108 | 52.757573839855375 | 11.918434293018482 | 0.32244092343412517 | 85.47902965290235 | 37.76294123158168 | 10.598142654253946 | 9.245486988412365 | 6.145155915970048 | 3.2554788671183146 |
| user_005 | Jogging | 1.446046538826e12 | -7.6042e-2 | -3.48655 | -0.1922 | -5.848474090909092 | -1.5147946363636364 | -3.268276545454546 | 5.782385764e-3 | 12.1560309025 | 3.694084e-2 | 3.4926714887409607 | 8.79684486571532 | 5.772432090909092 | 1.9717553636363634 | 3.076076545454546 | 7.780923876033058 | 2.39739494214876 | 3.527368603305786 | 33.32097224415711 | 3.8878192140287675 | 9.462246913495573 | 76.2985903174133 | 7.323591190988912 | 15.880524485696595 | 8.73490642865814 | 2.706213441506215 | 3.9850375764472528 |
| user_005 | Jogging | 1.446046538939e12 | 0.11556 | -19.58108 | -11.68829 | 10.354050363636363 | -19.23271363636363 | -4.720963272727273 | 1.3354113599999998e-2 | 383.4186939664 | 136.6161231241 | 22.804564701043954 | 23.674808329678697 | 10.238490363636362 | 0.3483663636363694 | 6.967326727272727 | 8.065950421487605 | 6.036874181818183 | 4.233919008264463 | 104.82668492627465 | 0.12135912331322717 | 48.54364172456889 | 87.57518667666108 | 52.2563347149799 | 22.62891425410126 | 9.358161500885796 | 7.228854315517771 | 4.756985837071754 |
| user_005 | Jogging | 1.446046538947e12 | -3.94639 | -19.58108 | -8.85259 | 10.044004545454545 | -19.570629090909087 | -5.835736 | 15.5739940321 | 383.4186939664 | 78.36834970809998 | 21.84859349492777 | 24.18511751724803 | 13.990394545454546 | 1.0450909090913285e-2 | 3.0168539999999995 | 9.269081396694215 | 5.096284652892563 | 4.0549853140495875 | 195.7311395374843 | 1.0922150082653395e-4 | 9.101408057315997 | 105.31697516413934 | 42.50487940249286 | 21.1970906556682 | 10.26240591499573 | 6.519576627549742 | 4.604029827843017 |
| user_005 | Jogging | 1.446046538979e12 | -4.9044 | -13.48815 | 4.48288 | 3.7316036363636367 | -18.936601818181813 | -4.727930727272727 | 24.05313936 | 181.93019042249998 | 20.0962130944 | 15.03594170236437 | 22.799217118887203 | 8.636003636363636 | 5.448451818181814 | 9.210810727272726 | 10.874417636363638 | 2.1022333057851252 | 4.469224636363635 | 74.58055880728594 | 29.68562721504872 | 84.83903425364232 | 137.5093735084357 | 9.196215785575577 | 26.529507394367275 | 11.726439080489682 | 3.0325263041852706 | 5.150680284619428 |
| user_005 | Jogging | 1.446046539076e12 | 2.22318 | 4.714 | 7.58682 | 3.337951181818182 | 8.69234272727273 | 7.287228181818182 | 4.9425293124000005 | 22.221796000000005 | 57.559837712400004 | 9.204572940924528 | 12.255265287128658 | 1.114771181818182 | 3.9783427272727288 | 0.2995918181818187 | 3.6958518016528927 | 12.376827272727269 | 4.278889537190082 | 1.2427147878123062 | 15.827210855643813 | 8.97552575214879e-2 | 22.984673684992117 | 209.9559773255614 | 26.829853647783384 | 4.794233378235995 | 14.489857740004261 | 5.179754207275031 |
| user_005 | Jogging | 1.446046539133e12 | -1.83878 | -5.82409 | -7.7413 | -0.1352640909090908 | 1.7459141818181818 | 1.9432845454545458 | 3.3811118884000004 | 33.9200243281 | 59.927725689999995 | 9.860469659529407 | 6.6261052838034376 | 1.7035159090909093 | 7.570004181818182 | 9.684584545454545 | 2.386311570247934 | 5.564680247933885 | 4.438188 | 2.9019664525258273 | 57.30496331274476 | 93.79117781805701 | 7.486160788873022 | 35.40458672840595 | 28.815742980033075 | 2.7360849381685908 | 5.950175352744316 | 5.36802971117272 |
| user_005 | Jogging | 1.446046539158e12 | 1.91661 | -15.67241 | -16.55497 | -0.5393703636363636 | -3.3263021818181815 | -4.480592727272727 | 3.6733938920999996 | 245.62443520809998 | 274.06703170090003 | 22.877168985718054 | 9.004548610586447 | 2.4559803636363635 | 12.346107818181817 | 12.074377272727274 | 2.4233651074380167 | 7.4813292396694235 | 7.8037251239669425 | 6.031839546567404 | 152.4263782581702 | 145.79058652415293 | 7.831012942022457 | 59.9454553949351 | 78.68714381250022 | 2.7983947080464646 | 7.742445052755305 | 8.870577422721714 |
| user_005 | Jogging | 1.446046539262e12 | -3.29495 | -8.96635 | 8.08499 | 7.434739363636364 | -11.014747272727275 | 7.938672727272728 | 10.856695502500001 | 80.3954323225 | 65.36706330009999 | 12.514758931961094 | 18.097212429523772 | 10.729689363636364 | 2.048397272727275 | 0.14631727272727169 | 8.480824768595042 | 2.806567272727272 | 8.609085867768597 | 115.12623384013132 | 4.195931386916539 | 2.1408744298346803e-2 | 87.08888798453219 | 11.310359394584937 | 97.52241800503252 | 9.332142732756084 | 3.363087776818342 | 9.875343943632167 |
| user_005 | Jogging | 1.446046539302e12 | -5.90073 | -19.58108 | 9.00468 | 0.3803184545454543 | -14.035085454545454 | 8.408968181818182 | 34.8186145329 | 383.4186939664 | 81.0842619024 | 22.345504478567943 | 17.498293291626553 | 6.281048454545455 | 5.545994545454546 | 0.5957118181818188 | 7.303660975206612 | 3.1999061157024795 | 3.8785865289256205 | 39.451569688347845 | 30.758055498211576 | 0.3548725703214884 | 61.10605286841874 | 15.428117018312696 | 27.387089683350485 | 7.8170360667211165 | 3.927864180227302 | 5.233267591414611 |
| user_005 | Jogging | 1.446046539319e12 | -12.76007 | -19.58108 | -0.996927 | -3.8209815454545453 | -14.57853727272727 | 6.8204166363636345 | 162.81938640490003 | 383.4186939664 | 0.993863443329 | 23.39298920220819 | 17.550602729410397 | 8.939088454545455 | 5.002542727272729 | 7.817343636363635 | 8.1581096446281 | 3.2246087603305784 | 3.2974476859504134 | 79.90730239818785 | 25.025433738189275 | 61.11086152899502 | 70.00862498085486 | 15.035771101271676 | 18.016229528635986 | 8.367115690657974 | 3.877598625602149 | 4.244552924471079 |
| user_005 | Jogging | 1.4460465394e12 | 7.39642 | -2.56686 | -6.85994 | 2.146536363636363 | -14.540217272727268 | -8.664473363636363 | 54.7070288164 | 6.5887702596 | 47.0587768036 | 10.409350406226125 | 20.08056548210617 | 5.249883636363637 | 11.973357272727268 | 1.804533363636363 | 8.138792611570247 | 5.077282975206612 | 7.848378743801652 | 27.561278195358685 | 143.36128438037096 | 3.256340660476766 | 80.77637375669282 | 37.16680859849946 | 89.7226633472793 | 8.987567733079558 | 6.0964586932496685 | 9.472204777520348 |
| user_005 | Jogging | 1.446046539578e12 | 7.20482 | -19.58108 | -5.59536 | 0.6590119999999999 | -9.952231818181817 | -0.3907689090909091 | 51.909431232399996 | 383.4186939664 | 31.308053529600002 | 21.601763324515893 | 10.362929003394015 | 6.545808 | 9.628848181818183 | 5.2045910909090916 | 4.776739033057851 | 5.110536950413223 | 4.43185461983471 | 42.847602372864 | 92.71471730850334 | 27.08776842357029 | 36.474788326173105 | 42.3693493244356 | 21.37286011656183 | 6.039436093392586 | 6.509174242900216 | 4.623079073146147 |
| user_005 | Jogging | 1.44604653961e12 | 19.16076 | -19.58108 | 1.83878 | 6.097012454545455 | -15.902331818181814 | -1.007377 | 367.1347237776 | 383.4186939664 | 3.3811118884000004 | 27.457868264532117 | 18.36803215234251 | 13.063747545454545 | 3.678748181818186 | 2.846157 | 5.852874611570247 | 6.96764694214876 | 3.697434991735537 | 170.66149993136963 | 13.533188185230609 | 8.100609668649 | 58.13333525565159 | 55.72582041356123 | 16.12637407479173 | 7.624521968992652 | 7.464972901060072 | 4.0157656897273935 |
| user_005 | Jogging | 1.446046539682e12 | -6.89706 | -9.50284 | 5.67081 | 3.9998454545454547 | -18.448889090909088 | -3.1358959090909093 | 47.5694366436 | 90.30396806560002 | 32.158086056100004 | 13.03961237020871 | 22.043440536798908 | 10.896905454545454 | 8.946049090909087 | 8.80670590909091 | 10.263827082644628 | 2.2273277685950434 | 3.8956880661157025 | 118.74254848530246 | 80.0317943369553 | 77.55806896921675 | 120.76651362675165 | 12.032200956290906 | 21.95008966866136 | 10.989381858264442 | 3.468746309012942 | 4.685092279631358 |
| user_005 | Jogging | 1.446046539755e12 | 0.345482 | 6.70665 | 10.88237 | 0.10510900000000002 | 6.06217509090909 | 8.36019909090909 | 0.119357812324 | 44.9791542225 | 118.4259768169 | 12.78766940656991 | 14.882968130300673 | 0.240373 | 0.6444749090909099 | 2.52217090909091 | 4.839443595041322 | 13.605293487603307 | 7.101292727272729 | 5.7779179129e-2 | 0.41534790844773656 | 6.361346094664468 | 39.33490163750689 | 251.6198299976049 | 54.91098830766759 | 6.27175427113554 | 15.862529117313068 | 7.4101948899922725 |
| user_005 | Jogging | 1.446046539772e12 | 1.87829 | 3.87095 | 8.73643 | 1.6692744545454545 | 9.27411418181818 | 9.293819999999998 | 3.5279733241 | 14.9842539025 | 76.3252091449 | 9.738451436008704 | 13.889704399334034 | 0.20901554545454548 | 5.403164181818179 | 0.557389999999998 | 2.7011064214876033 | 13.387405983471075 | 5.818670082644629 | 4.368749824166117e-2 | 29.19418317568291 | 0.31068361209999784 | 12.831011815806407 | 247.56773383231638 | 43.55359550083381 | 3.5820401750687285 | 15.734285297792091 | 6.599514792833926 |
| user_005 | Jogging | 1.446046539942e12 | 0.537083 | -9.27292 | 11.76374 | 6.584724818181818 | -9.77108 | 4.778988181818182 | 0.28845814888899995 | 85.98704532639998 | 138.3855787876 | 14.988698484621304 | 16.5450927999781 | 6.047641818181818 | 0.4981600000000004 | 6.9847518181818185 | 4.964222173553718 | 3.317082661157025 | 9.577860421487607 | 36.57397156102149 | 0.24816338560000037 | 48.78675796159422 | 37.43595558677263 | 25.67015866625859 | 127.0524021856521 | 6.118492917931068 | 5.066572674526498 | 11.271752400831563 |
| user_005 | Jogging | 1.44604653999e12 | -0.804128 | -19.58108 | 5.40257 | 3.156799727272728 | -10.986879090909092 | 7.015502727272727 | 0.646621840384 | 383.4186939664 | 29.187762604899998 | 20.32862706657004 | 15.52897945485492 | 3.960927727272728 | 8.594200909090908 | 1.6129327272727272 | 5.840839694214875 | 2.2605820661157026 | 4.385301074380165 | 15.6889484606779 | 73.86028926581899 | 2.601551982707438 | 40.40700436750239 | 12.320737220128098 | 25.776883221690685 | 6.356650404694472 | 3.5100907709243216 | 5.077093974085046 |
| user_005 | Jogging | 1.446046540096e12 | 9.8106 | 0.613724 | -5.25048 | 2.376456363636364 | -13.192039636363639 | -8.350943636363636 | 96.24787236000002 | 0.37665714817600005 | 27.567540230399995 | 11.14414957448867 | 18.601696635532743 | 7.434143636363637 | 13.805763636363638 | 3.100463636363637 | 6.501151743801654 | 6.114781735537192 | 7.790107867768596 | 55.266491606085964 | 190.59910958314055 | 9.612874760413225 | 54.78844817181653 | 52.86952324153863 | 86.24638087968755 | 7.401921924190806 | 7.271143186703081 | 9.286892961571569 |
| user_005 | Jogging | 1.446046540104e12 | 2.2615 | 1.95493 | -8.66099 | 3.400654545454545 | -11.234220545454544 | -8.657506363636365 | 5.114382249999999 | 3.8217513049000003 | 75.0127477801 | 9.16236221369795 | 17.417205621653796 | 1.139154545454545 | 13.189150545454545 | 3.483636363634801e-3 | 5.963083008264463 | 6.803280214876033 | 6.720622851239669 | 1.2976730784297512 | 173.95369211066392 | 1.21357223140387e-5 | 50.3778636743197 | 65.81661171360078 | 73.65714927952101 | 7.097736517673765 | 8.112743784540516 | 8.582374338114192 |
| user_005 | Jogging | 1.44604654012e12 | -13.06663 | -2.2603 | -14.94552 | 2.752692727272727 | -7.8515822727272715 | -9.939495454545453 | 170.7368195569 | 5.1089560899999995 | 223.36856807040002 | 19.98034893882737 | 16.54993111786699 | 15.819322727272727 | 5.5912822727272715 | 5.006024545454547 | 7.762237380165288 | 7.418938900826446 | 5.458586206611571 | 250.25097154960744 | 31.26243745331424 | 25.060281749693406 | 79.2542529639753 | 73.90660281971599 | 53.25524326196705 | 8.902485774432627 | 8.59689495223223 | 7.297619013210203 |
| user_005 | Jogging | 1.446046540161e12 | -9.69444 | -3.14167 | -4.9056 | -5.496624545454545 | -2.319522272727273 | -7.13166090909091 | 93.9821669136 | 9.8700903889 | 24.064911359999996 | 11.310047244043679 | 15.320231174103524 | 4.197815454545455 | 0.8221477272727271 | 2.2260609090909105 | 11.350412371900823 | 6.493867867768595 | 3.1625339338842977 | 17.621654590420665 | 0.6759268854597105 | 4.955347170982651 | 159.6364509893014 | 66.65468024152023 | 12.332436510854023 | 12.63473193183383 | 8.164231760644734 | 3.5117568980289655 |
| user_005 | Jogging | 1.446046540193e12 | 1.41845 | -3.52487 | -0.307161 | -9.380910363636366 | -3.1695362727272727 | -7.542732818181817 | 2.0120004025 | 12.424708516899999 | 9.434787992100001e-2 | 3.8119623292106386 | 14.2613084737019 | 10.799360363636366 | 0.3553337272727273 | 7.235571818181818 | 10.904820504132232 | 2.8220859338842974 | 3.948575702479339 | 116.62618426368017 | 0.12626205773752894 | 52.353499536066934 | 151.38493928892447 | 15.014976780614317 | 17.83407046135147 | 12.303858715416252 | 3.8749163578862342 | 4.22304042857175 |
| user_005 | Jogging | 1.446046540217e12 | -2.29862 | -6.7821 | 0.689167 | -6.287415909090908 | -4.050903636363636 | -3.8430809090909097 | 5.2836539044 | 45.996880409999996 | 0.47495115388899994 | 7.1941285412681495 | 10.53827068016974 | 3.9887959090909075 | 2.7311963636363634 | 4.53224790909091 | 8.581849818181821 | 1.5055775371900824 | 4.445789826446281 | 15.91049280438036 | 7.459433576740495 | 20.541271109458926 | 97.87125545609757 | 3.4811748435715795 | 22.0431335507613 | 9.892990218134129 | 1.865790675175428 | 4.695011560237238 |
| user_005 | Jogging | 1.446046540339e12 | -2.10702 | -19.58108 | -9.65732 | 9.967363636363636 | -19.52882545454545 | -3.710701727272727 | 4.439533280399999 | 383.4186939664 | 93.2638295824 | 21.934494679139522 | 23.708771637632957 | 12.074383636363637 | 5.225454545454866e-2 | 5.946618272727273 | 9.42869852892562 | 4.468590330578514 | 4.145560669421488 | 145.79074019808596 | 2.730537520661492e-3 | 35.36226888153389 | 110.2410839227691 | 32.07802959815651 | 22.283775548103858 | 10.499575416309419 | 5.6637469574616865 | 4.720569409308993 |
| user_005 | Jogging | 1.446046540514e12 | 5.13552 | -2.79678 | -7.89458 | -2.3648110000000004 | -7.228005272727272 | -7.724018181818183e-2 | 26.373565670399998 | 7.8219783684 | 62.3243933764 | 9.824456087499195 | 16.574582047151853 | 7.500331 | 4.431225272727272 | 7.817339818181819 | 9.026175991735538 | 8.765533487603308 | 6.827665371900826 | 56.254965109561 | 19.63575741765689 | 61.11080183293095 | 104.91049056532684 | 167.91245901524488 | 57.88847597379886 | 10.2425822215556 | 12.958103989984217 | 7.608447671752685 |
| user_005 | Jogging | 1.446046540563e12 | 4.33079 | -10.57581 | 1.14901 | 1.3104566363636363 | -4.935754363636364 | -4.727931818181819 | 18.7557420241 | 111.84775715610002 | 1.3202239801000002 | 11.485805290022116 | 16.556757493226794 | 3.020333363636364 | 5.640055636363637 | 5.87694181818182 | 3.7015521404958673 | 12.720126892561984 | 11.229750066115702 | 9.122413627494954 | 31.81022758127723 | 34.53844513429423 | 17.429640415905563 | 216.0190225727229 | 139.08506675730447 | 4.174882084072023 | 14.697585603517432 | 11.793433204851947 |
| user_005 | Jogging | 1.446046540717e12 | -6.51385 | -19.58108 | -6.43841 | -2.0268926363636366 | -14.97219181818182 | 4.50029790909091 | 42.430241822499994 | 383.4186939664 | 41.453123328100006 | 21.61717046972152 | 17.517867329058955 | 4.486957363636363 | 4.60888818181818 | 10.93870790909091 | 5.2619166611570245 | 3.4266593388429754 | 4.726067801652892 | 20.132786383090583 | 21.241850272503292 | 119.65533072040803 | 35.40847356546848 | 18.10052390631796 | 32.93065278676701 | 5.950501959118111 | 4.254471048945798 | 5.738523572032009 |
| user_005 | Jogging | 1.446046540725e12 | -2.2603 | -19.58108 | -13.68095 | -1.9711544545454545 | -15.864009999999997 | 2.100052454545455 | 5.1089560899999995 | 383.4186939664 | 187.1683929025 | 23.993666726011263 | 18.21761657089616 | 0.2891455454545455 | 3.717070000000003 | 15.781002454545455 | 4.390684289256199 | 3.647080165289256 | 5.649555338842974 | 8.360514645620665e-2 | 13.816609384900024 | 249.04003847036967 | 26.55514334235929 | 19.20472444099144 | 52.69664942424522 | 5.153168281975594 | 4.382319527486722 | 7.259245788940144 |
| user_005 | Jogging | 1.446046540822e12 | -17.85667 | -2.6435 | -5.71033 | -1.9746390909090907 | -4.9949745454545456 | -9.228827272727273 | 318.86066348890006 | 6.98809225 | 32.6078687089 | 18.932950759134194 | 15.92906364072432 | 15.882030909090911 | 2.3514745454545456 | 3.5184972727272728 | 9.308036595041322 | 7.645694710743802 | 3.4605465619834708 | 252.2389057973191 | 5.529432537920662 | 12.379823058189256 | 111.33812321837313 | 84.26482645558666 | 22.85573815537694 | 10.551688169121238 | 9.179587488312677 | 4.780767527853341 |
| user_005 | Jogging | 1.44604654083e12 | -14.75272 | -3.21831 | -3.14286 | -3.5666736363636358 | -3.8558163636363645 | -8.222047272727272 | 217.6427473984 | 10.357519256099998 | 9.877568979600001 | 15.42328874248615 | 15.386100335157364 | 11.186046363636365 | 0.6375063636363647 | 5.079187272727271 | 9.959798661157025 | 7.569370495867769 | 2.874974024793388 | 125.12763324942235 | 0.40641436367686085 | 25.798143351434696 | 121.24667289853097 | 84.10343286667974 | 13.135425839644437 | 11.011206695840878 | 9.170792379433728 | 3.6242828034860133 |
| user_005 | Jogging | 1.446046540838e12 | -10.72909 | -3.25663 | -3.2195 | -4.94968909090909 | -2.831619090909091 | -7.389450909090908 | 115.11337222809999 | 10.6056389569 | 10.36518025 | 11.66551290921235 | 14.664609256904741 | 5.779400909090909 | 0.4250109090909091 | 4.169950909090908 | 9.997485338842976 | 7.3236142148760335 | 2.544659586776859 | 33.401474868000825 | 0.180634272846281 | 17.388490584228094 | 121.66666259466352 | 83.23017701976578 | 9.180451949874753 | 11.030261220599607 | 9.123057438148999 | 3.029926063433686 |
| user_005 | Jogging | 1.446046540927e12 | -4.40624 | -6.24561 | 0.497565 | -2.3682936363636364 | -2.302102909090909 | -3.285694818181818 | 19.414950937600004 | 39.0076442721 | 0.24757092922499999 | 7.659645301117083 | 5.719106541657371 | 2.037946363636364 | 3.9435070909090912 | 3.783259818181818 | 4.485693983471075 | 1.3127083719008263 | 3.0485239504132235 | 4.153225381058679 | 15.551248176050283 | 14.313054851869122 | 31.307420746990218 | 2.6778831208879526 | 12.53341286799084 | 5.595303454415159 | 1.6364238817885641 | 3.540256045541175 |
| user_005 | Jogging | 1.446046540976e12 | 3.2195 | -19.58108 | -6.9749 | -3.1137981818181815 | -10.206538636363636 | -1.711078363636364 | 10.36518025 | 383.4186939664 | 48.64923001 | 21.034093853227905 | 11.824510576150509 | 6.333298181818181 | 9.374541363636364 | 5.263821636363636 | 2.541175404958677 | 6.296882462809918 | 2.6064149173553717 | 40.11066585982148 | 87.88202577852914 | 27.707818219449944 | 9.363454819684579 | 59.75930482568696 | 9.126199517026393 | 3.0599762776342856 | 7.730414272578602 | 3.020960032345081 |
| user_005 | Jogging | 1.446046541161e12 | 3.33447 | 3.52607 | 4.7128 | 1.1502080909090908 | 9.664284545454546 | 10.102029090909092 | 11.1186901809 | 12.4331696449 | 22.21048384 | 6.764787037727056 | 14.376779236094603 | 2.1842619090909094 | 6.138214545454546 | 5.389229090909092 | 3.1340315041322313 | 12.022759669421486 | 6.602495041322314 | 4.771000087505464 | 37.677677806029756 | 29.04379019430084 | 12.984159483889442 | 206.43890998226743 | 57.00039092718214 | 3.60335392154163 | 14.367982112400734 | 7.549860325011459 |
| user_005 | Jogging | 1.446046541235e12 | 7.58802 | -19.58108 | -19.54396 | 2.4949023636363634 | -3.928976181818182 | -3.397172818181819 | 57.578047520400006 | 383.4186939664 | 381.96637248159993 | 28.687333685241644 | 9.474860604030479 | 5.093117636363637 | 15.652103818181818 | 16.14678718181818 | 2.017675578512397 | 8.562214818181818 | 8.995139520661157 | 25.93984725783832 | 244.98835393514184 | 260.71873629492785 | 5.3878930097540945 | 83.3646869357908 | 98.37953343385551 | 2.3211835364214726 | 9.130426437784317 | 9.918645745960257 |
| user_005 | Jogging | 1.44604654134e12 | -1.57053 | -7.85507 | 11.4955 | 3.2404081818181822 | -8.774752727272727 | 9.586448181818183 | 2.4665644809 | 61.70212470490001 | 132.14652025 | 14.011252957383933 | 15.148865444150692 | 4.810938181818182 | 0.9196827272727264 | 1.9090518181818172 | 4.711814024793388 | 2.3742772479338843 | 9.278583223140494 | 23.145126189276034 | 0.8458163188438 | 3.6444788445033023 | 27.561006826288562 | 7.091636321663279 | 126.34631874555262 | 5.249857791053826 | 2.66301264016213 | 11.240387837861851 |
| user_005 | Jogging | 1.446046541412e12 | 6.59169 | -19.58108 | -15.52033 | -1.9885736363636364 | -16.68963909090909 | -0.8122936363636359 | 43.450377056099995 | 383.4186939664 | 240.88064330889998 | 25.840853591385095 | 21.518287655835156 | 8.580263636363636 | 2.8914409090909103 | 14.708036363636364 | 5.809802975206612 | 4.999057190082644 | 9.224111239669421 | 73.62092406950413 | 8.36043053076447 | 216.32633367404958 | 39.01585487781022 | 30.637290880641924 | 151.2366587860819 | 6.246267275566281 | 5.535096284676711 | 12.297831466810802 |
| user_005 | Jogging | 1.446046541517e12 | -9.6178 | -1.57053 | -5.4804 | -3.333268181818182 | -1.7725854545454547 | -7.323260909090909 | 92.50207684000002 | 2.4665644809 | 30.034784160000005 | 11.180493078612411 | 16.414639162431733 | 6.284531818181819 | 0.2020554545454547 | 1.8428609090909083 | 12.730577107438016 | 7.694465123966942 | 2.576645785123967 | 39.49534017373968 | 4.082640671157031e-2 | 3.396136330255369 | 192.85396690384394 | 111.32270980228442 | 8.473371684080687 | 13.887187148729721 | 10.550957767060032 | 2.9109056467155865 |
| user_005 | Jogging | 1.446046541526e12 | -5.24928 | -1.37893 | -9.619 | -4.914851818181818 | -0.8563818181818182 | -7.8109745454545445 | 27.5549405184 | 1.9014479449 | 92.525161 | 11.044525769054097 | 15.8521414573526 | 0.3344281818181818 | 0.5225481818181817 | 1.8080254545454553 | 11.748183140495867 | 7.11649305785124 | 2.5883634710743793 | 0.11184220879421486 | 0.27305660232148754 | 3.2689560442843 | 181.58080947832903 | 107.04410449639293 | 8.514233087209762 | 13.475192372590792 | 10.34621208444873 | 2.9179158807631453 |
| user_005 | Jogging | 1.446046541542e12 | 0.307161 | -2.68182 | -3.87095 | -7.788874454545455 | -1.243068181818182 | -8.194178181818181 | 9.434787992100001e-2 | 7.192158512400001 | 14.9842539025 | 4.719190639804775 | 14.113925983821156 | 8.096035454545456 | 1.4387518181818182 | 4.32322818181818 | 10.823112314049588 | 4.726066363636364 | 2.2985858677685944 | 65.54579008125705 | 2.0700067943214875 | 18.69030191206693 | 164.5818232479703 | 64.88748743124589 | 7.553243462068892 | 12.828944744131151 | 8.055276992831836 | 2.74831647778579 |
| user_005 | Jogging | 1.446046541567e12 | -3.67815 | -1.53221 | -1.64837 | -7.945640363636364 | -2.6504690909090916 | -5.546593272727272 | 13.5287874225 | 2.3476674841 | 2.7171236568999997 | 4.312027198835833 | 10.93577854426604 | 4.267490363636364 | 1.1182590909090915 | 3.8982232727272725 | 10.032953694214877 | 1.1673442975206612 | 3.1954715867768595 | 18.211474003729226 | 1.2505033944008277 | 15.196144684032527 | 144.1504480278062 | 1.9028610728867017 | 14.167965256505674 | 12.006267031338517 | 1.3794423050228313 | 3.7640357671660976 |
| user_005 | Jogging | 1.44604654159e12 | -7.3569 | -7.81675 | 3.94639 | -4.799891272727273 | -2.9047763636363633 | -2.5401904545454546 | 54.123977610000004 | 61.1015805625 | 15.5739940321 | 11.436763187397036 | 7.609283436074653 | 2.557008727272727 | 4.911973636363637 | 6.486580454545455 | 5.092799677685949 | 1.3836494214876036 | 4.091723454545454 | 6.538293631348892 | 24.12748500433141 | 42.075725993291115 | 37.86155491294937 | 3.7278022676234417 | 20.641978826787177 | 6.153174376933371 | 1.9307517364030626 | 4.543344453900362 |
| user_005 | Jogging | 1.446046541647e12 | 12.68462 | -19.58108 | -8.23947 | -3.9045899999999993 | -12.63116909090909 | 1.8352919090909092 | 160.89958454440003 | 383.4186939664 | 67.88886588090001 | 24.742820057376242 | 15.833626551545684 | 16.58921 | 6.94991090909091 | 10.07476190909091 | 5.423432454545455 | 6.832734214876033 | 5.923814867768595 | 275.20188842410005 | 48.301261644300844 | 101.50082752486911 | 49.805850736926025 | 62.230873282694525 | 41.61672815250327 | 7.057326033061391 | 7.888654719449605 | 6.451102863270998 |
| user_005 | Jogging | 1.446046541704e12 | -0.804128 | -19.58108 | -2.03158 | 8.35791109090909 | -19.581079999999996 | -0.5022472727272728 | 0.646621840384 | 383.4186939664 | 4.127317296399999 | 19.70260472889775 | 23.37056805963689 | 9.16203909090909 | 3.552713678800501e-15 | 1.529332727272727 | 10.50261695041322 | 4.642774710743804 | 4.055621512396694 | 83.94296030334627 | 1.2621774483536189e-29 | 2.3388585907074373 | 146.0293819989988 | 37.699085315443725 | 26.298503606760573 | 12.084261748199548 | 6.1399580874338 | 5.128206665761489 |
| user_005 | Jogging | 1.446046541777e12 | 2.4531 | 19.54396 | 15.94065 | -1.5252460909090912 | -3.827948181818181 | 6.688037636363637 | 6.01769961 | 381.96637248159993 | 254.1043224225 | 25.33946318519988 | 18.518431558431583 | 3.978346090909091 | 23.371908181818178 | 9.252612363636363 | 7.945605561983471 | 11.386833388429755 | 5.833873041322314 | 15.827237619051646 | 546.2460920593395 | 85.61083555171648 | 88.88585335568426 | 235.8254541440598 | 43.61219456277871 | 9.42792943098771 | 15.356609461207894 | 6.603952949770214 |
- Prediction using Random Forests
Instead of just stopping at ETL and feature engineering let's quickly see without understanding the random forest model in detail how simple it is to predict the activity using random forest model.
val splits = dataFeatDF.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))
splits: Array[org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]] = Array([user_id: string, activity: string ... 27 more fields], [user_id: string, activity: string ... 27 more fields])
trainingData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
testData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
See
import org.apache.spark.ml.feature.{StringIndexer,VectorAssembler}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
val transformers = Array(
new StringIndexer().setInputCol("activity").setOutputCol("label"),
new VectorAssembler()
.setInputCols(Array("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ"))
.setOutputCol("features")
)
// Train a RandomForest model.
val rf = new RandomForestClassifier()
.setLabelCol("label")
.setFeaturesCol("features")
.setNumTrees(10)
.setFeatureSubsetStrategy("auto")
.setImpurity("gini")
.setMaxDepth(20)
.setMaxBins(32)
.setSeed(12345)
val model = new Pipeline().setStages(transformers :+ rf).fit(trainingData)
import org.apache.spark.ml.feature.{StringIndexer, VectorAssembler}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
transformers: Array[org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable}}] = Array(strIdx_84996728c97a, VectorAssembler: uid=vecAssembler_e11ceeee69a9, handleInvalid=error, numInputCols=6)
rf: org.apache.spark.ml.classification.RandomForestClassifier = rfc_6ab6b8d21734
model: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
Before we go further let's find quickly which label is mapped to which activity by the StringIndexer and quickly check that the estimator we have built can transform the union of the training and test data. This is mostly for debugging... not something you would do in a real pipeline except during debugging stages.
val activityLabelDF = model.transform(trainingData.union(testData)).select("activity","label").distinct
display(activityLabelDF) // this just shows what activity is associated with what label
| activity | label |
|---|---|
| Jogging | 0.0 |
| Jumping | 4.0 |
| Standing | 1.0 |
| Walking | 2.0 |
| Sitting | 3.0 |
val myPredictionsOnTestData = model.transform(testData)
display(myPredictionsOnTestData)
Let's evaluate accuracy at a lower level...
val accuracy: Double = 1.0 * model.transform(testData)
.select("activity","label","prediction")
.filter($"label"===$"prediction").count() / testData.count()
accuracy: Double = 0.9828725226327379
We get 98% correct predictions and here are the mis-predicted ones.
display(model.transform(testData).select("activity","label","prediction").filter(not($"label"===$"prediction")))
| activity | label | prediction |
|---|---|---|
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 1.0 |
| Jogging | 0.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Jumping | 4.0 | 2.0 |
| Walking | 2.0 | 1.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 2.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Jogging | 0.0 | 2.0 |
| Jogging | 0.0 | 2.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 2.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 2.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
testData.printSchema()
root
|-- user_id: string (nullable = true)
|-- activity: string (nullable = true)
|-- timeStampAsLong: long (nullable = true)
|-- x: double (nullable = true)
|-- y: double (nullable = true)
|-- z: double (nullable = true)
|-- meanX: double (nullable = true)
|-- meanY: double (nullable = true)
|-- meanZ: double (nullable = true)
|-- SqX: double (nullable = true)
|-- SqY: double (nullable = true)
|-- SqZ: double (nullable = true)
|-- resultant: double (nullable = true)
|-- meanResultant: double (nullable = true)
|-- absDevFromMeanX: double (nullable = true)
|-- absDevFromMeanY: double (nullable = true)
|-- absDevFromMeanZ: double (nullable = true)
|-- meanAbsDevFromMeanX: double (nullable = true)
|-- meanAbsDevFromMeanY: double (nullable = true)
|-- meanAbsDevFromMeanZ: double (nullable = true)
|-- sqrDevFromMeanX: double (nullable = true)
|-- sqrDevFromMeanY: double (nullable = true)
|-- sqrDevFromMeanZ: double (nullable = true)
|-- varianceX: double (nullable = true)
|-- varianceY: double (nullable = true)
|-- varianceZ: double (nullable = true)
|-- stddevX: double (nullable = true)
|-- stddevY: double (nullable = true)
|-- stddevZ: double (nullable = true)
Let's understand the mis-predicitons better by seeing the user_id also.
display(model.transform(testData).select("user_id","activity","label","prediction").filter(not($"label"===$"prediction")))
| user_id | activity | label | prediction |
|---|---|---|---|
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Walking | 2.0 | 1.0 |
| user_001 | Walking | 2.0 | 1.0 |
| user_001 | Walking | 2.0 | 1.0 |
| user_002 | Jogging | 0.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_003 | Jumping | 4.0 | 2.0 |
| user_003 | Walking | 2.0 | 1.0 |
| user_004 | Jogging | 0.0 | 4.0 |
| user_004 | Jogging | 0.0 | 4.0 |
| user_004 | Jumping | 4.0 | 2.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_005 | Jogging | 0.0 | 2.0 |
| user_005 | Jogging | 0.0 | 2.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 2.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jumping | 4.0 | 2.0 |
| user_005 | Jumping | 4.0 | 0.0 |
| user_005 | Jumping | 4.0 | 0.0 |
| user_005 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 1.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
Let's evaluate our model's accuracy using ML pipeline's evaluation.MulticlassClassificationEvaluator:
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// Select (prediction, true label) and compute test error.
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy = evaluator.evaluate(myPredictionsOnTestData)
println(s"Test Error = ${(1.0 - accuracy)}")
Test Error = 0.017127477367262056
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_0923535d6e49, metricName=accuracy, metricLabel=0.0, beta=1.0, eps=1.0E-15
accuracy: Double = 0.9828725226327379
Note, it's the same 98% we got from our slightly lower-level operations earlier for accuracy.
Think!
What else do you need to investigate to try to understand the mis-predicitons better?
- time-line?
- are mis-predictions happening during transitions between users or activities of the same user or both, as our Markov process simply uses the same sliding window over the time-odered, user-grouped, activity streams...
- ...
Typically, in inustry you will have to improve on an existing algorithm that is in production and getting into the details under the given constraints needs some thinking from scratch over the entire data science pipeline and process.
Explaining the Model's Predictions to a Curious Client whose Activity is being Predicted
Now, let's try to explain the decision branches in each tree of our best-fitted random forest model. Often, being able to "explain" why the model "predicts" is critical for various businesses. how it predicts.
import org.apache.spark.ml.classification.RandomForestClassificationModel
val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
println(s"Learned classification forest model:\n ${rfModel.toDebugString}")
Learned classification forest model:
RandomForestClassificationModel: uid=rfc_6ab6b8d21734, numTrees=10, numClasses=5, numFeatures=6
Tree 0 (weight 1.0):
If (feature 3 <= 0.45832723868728054)
If (feature 1 <= -8.097179727272728)
If (feature 0 <= -7.121753363636364)
Predict: 2.0
Else (feature 0 > -7.121753363636364)
If (feature 5 <= 0.7848413904200169)
If (feature 1 <= -8.4067287012987)
If (feature 1 <= -11.596521363636366)
Predict: 2.0
Else (feature 1 > -11.596521363636366)
If (feature 1 <= -9.49238863636364)
If (feature 0 <= -1.976380909090909)
Predict: 2.0
Else (feature 0 > -1.976380909090909)
Predict: 1.0
Else (feature 1 > -9.49238863636364)
Predict: 1.0
Else (feature 1 > -8.4067287012987)
If (feature 0 <= -3.502226363636364)
Predict: 2.0
Else (feature 0 > -3.502226363636364)
Predict: 1.0
Else (feature 5 > 0.7848413904200169)
Predict: 2.0
Else (feature 1 > -8.097179727272728)
If (feature 2 <= 2.943098181818182)
If (feature 4 <= 0.904496986362019)
If (feature 3 <= 0.027620762421071275)
If (feature 0 <= -3.502226363636364)
Predict: 0.0
Else (feature 0 > -3.502226363636364)
If (feature 2 <= -0.045886318181818195)
If (feature 1 <= -6.820416818181818)
Predict: 4.0
Else (feature 1 > -6.820416818181818)
Predict: 0.0
Else (feature 2 > -0.045886318181818195)
Predict: 4.0
Else (feature 3 > 0.027620762421071275)
Predict: 4.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 2 > 2.943098181818182)
If (feature 3 <= 0.2198889879412786)
If (feature 0 <= -1.105464818181818)
Predict: 2.0
Else (feature 0 > -1.105464818181818)
If (feature 3 <= 0.12199305012399356)
Predict: 3.0
Else (feature 3 > 0.12199305012399356)
If (feature 1 <= -0.6543302727272726)
Predict: 3.0
Else (feature 1 > -0.6543302727272726)
Predict: 2.0
Else (feature 3 > 0.2198889879412786)
If (feature 0 <= -0.1735843181818182)
Predict: 2.0
Else (feature 0 > -0.1735843181818182)
Predict: 3.0
Else (feature 3 > 0.45832723868728054)
If (feature 4 <= 2.2658035906051546)
If (feature 5 <= 2.8353340431585474)
If (feature 5 <= 0.7848413904200169)
If (feature 1 <= -8.097179727272728)
If (feature 3 <= 1.5843984037196344)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 0 <= 2.8563333333333336)
If (feature 0 <= -4.357466363636364)
If (feature 1 <= -8.4067287012987)
If (feature 2 <= -1.0073777272727273)
Predict: 1.0
Else (feature 2 > -1.0073777272727273)
Predict: 2.0
Else (feature 1 > -8.4067287012987)
Predict: 1.0
Else (feature 0 > -4.357466363636364)
If (feature 0 <= -2.5093826818181824)
If (feature 2 <= -0.5126976818181816)
Predict: 1.0
Else (feature 2 > -0.5126976818181816)
Predict: 2.0
Else (feature 0 > -2.5093826818181824)
Predict: 1.0
Else (feature 0 > 2.8563333333333336)
If (feature 4 <= 0.904496986362019)
Predict: 1.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 3 > 1.5843984037196344)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 1 > -8.097179727272728)
If (feature 3 <= 2.037322122633077)
If (feature 1 <= -6.820416818181818)
If (feature 4 <= 0.904496986362019)
Predict: 4.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 1 > -6.820416818181818)
Predict: 2.0
Else (feature 3 > 2.037322122633077)
Predict: 0.0
Else (feature 5 > 0.7848413904200169)
If (feature 3 <= 3.2305826019123103)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 1 <= -17.407273636363634)
Predict: 0.0
Else (feature 1 > -17.407273636363634)
If (feature 1 <= -2.2306882272727275)
If (feature 0 <= -0.5846568636363636)
If (feature 4 <= 0.904496986362019)
If (feature 2 <= 0.41744104545454547)
If (feature 5 <= 1.6130554927490437)
Predict: 2.0
Else (feature 5 > 1.6130554927490437)
If (feature 3 <= 1.7795641834853977)
Predict: 2.0
Else (feature 3 > 1.7795641834853977)
If (feature 1 <= -9.105701681818182)
If (feature 5 <= 1.7614806708061252)
Predict: 2.0
Else (feature 5 > 1.7614806708061252)
If (feature 5 <= 1.9145738226248412)
If (feature 3 <= 2.283530423945612)
Predict: 2.0
Else (feature 3 > 2.283530423945612)
Predict: 1.0
Else (feature 5 > 1.9145738226248412)
Predict: 2.0
Else (feature 1 > -9.105701681818182)
If (feature 0 <= -3.065027181818182)
If (feature 0 <= -4.988009090909092)
Predict: 2.0
Else (feature 0 > -4.988009090909092)
If (feature 0 <= -3.502226363636364)
Predict: 1.0
Else (feature 0 > -3.502226363636364)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
Predict: 1.0
Else (feature 2 > 0.41744104545454547)
Predict: 2.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 0 > -0.5846568636363636)
If (feature 3 <= 1.0884225728208126)
If (feature 2 <= -0.045886318181818195)
Predict: 2.0
Else (feature 2 > -0.045886318181818195)
Predict: 1.0
Else (feature 3 > 1.0884225728208126)
If (feature 4 <= 0.904496986362019)
If (feature 1 <= -9.49238863636364)
Predict: 2.0
Else (feature 1 > -9.49238863636364)
If (feature 3 <= 1.7795641834853977)
If (feature 0 <= 0.32806340909090914)
Predict: 2.0
Else (feature 0 > 0.32806340909090914)
Predict: 1.0
Else (feature 3 > 1.7795641834853977)
Predict: 1.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 1 > -2.2306882272727275)
If (feature 2 <= 1.1333349545454543)
If (feature 0 <= 0.32806340909090914)
If (feature 1 <= 1.425418909090909)
Predict: 0.0
Else (feature 1 > 1.425418909090909)
Predict: 4.0
Else (feature 0 > 0.32806340909090914)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
Predict: 2.0
Else (feature 3 > 3.2305826019123103)
If (feature 2 <= -0.9551237727272728)
If (feature 5 <= 1.4557098485258626)
If (feature 5 <= 1.2559600735949519)
Predict: 0.0
Else (feature 5 > 1.2559600735949519)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 1.0
Else (feature 5 > 1.4557098485258626)
If (feature 3 <= 4.092746328718364)
If (feature 4 <= 1.9510708064802356)
If (feature 1 <= -11.596521363636366)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 4 <= 1.278422893229283)
Predict: 2.0
Else (feature 4 > 1.278422893229283)
Predict: 0.0
Else (feature 4 > 1.9510708064802356)
Predict: 2.0
Else (feature 3 > 4.092746328718364)
Predict: 0.0
Else (feature 2 > -0.9551237727272728)
If (feature 0 <= 2.8563333333333336)
If (feature 4 <= 0.904496986362019)
If (feature 3 <= 3.5937950153207483)
Predict: 1.0
Else (feature 3 > 3.5937950153207483)
Predict: 0.0
Else (feature 4 > 0.904496986362019)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 5 <= 2.600144428803959)
Predict: 2.0
Else (feature 5 > 2.600144428803959)
If (feature 4 <= 1.9510708064802356)
Predict: 0.0
Else (feature 4 > 1.9510708064802356)
Predict: 2.0
Else (feature 0 > 2.8563333333333336)
If (feature 3 <= 3.5937950153207483)
If (feature 0 <= 3.5330349999999995)
Predict: 2.0
Else (feature 0 > 3.5330349999999995)
Predict: 0.0
Else (feature 3 > 3.5937950153207483)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 3 <= 1.0884225728208126)
If (feature 0 <= -1.976380909090909)
Predict: 0.0
Else (feature 0 > -1.976380909090909)
Predict: 3.0
Else (feature 3 > 1.0884225728208126)
If (feature 4 <= 1.9510708064802356)
If (feature 3 <= 2.283530423945612)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 3 > 2.283530423945612)
Predict: 0.0
Else (feature 4 > 1.9510708064802356)
If (feature 1 <= -10.088093363636364)
If (feature 1 <= -17.407273636363634)
Predict: 0.0
Else (feature 1 > -17.407273636363634)
Predict: 2.0
Else (feature 1 > -10.088093363636364)
Predict: 0.0
Else (feature 4 > 2.2658035906051546)
If (feature 3 <= 3.2305826019123103)
If (feature 4 <= 4.527012376192168)
If (feature 0 <= 3.5330349999999995)
If (feature 0 <= -1.2744209090909089)
If (feature 2 <= -1.8347482272727271)
If (feature 5 <= 1.4557098485258626)
If (feature 3 <= 1.5843984037196344)
Predict: 4.0
Else (feature 3 > 1.5843984037196344)
Predict: 0.0
Else (feature 5 > 1.4557098485258626)
If (feature 0 <= -4.357466363636364)
If (feature 0 <= -6.212517272727274)
Predict: 0.0
Else (feature 0 > -6.212517272727274)
If (feature 5 <= 1.9145738226248412)
If (feature 0 <= -4.988009090909092)
If (feature 2 <= -2.198210545454545)
Predict: 0.0
Else (feature 2 > -2.198210545454545)
Predict: 2.0
Else (feature 0 > -4.988009090909092)
Predict: 2.0
Else (feature 5 > 1.9145738226248412)
Predict: 2.0
Else (feature 0 > -4.357466363636364)
If (feature 1 <= -9.356527272727273)
If (feature 4 <= 4.0067789684062625)
If (feature 5 <= 2.0408593821519982)
Predict: 0.0
Else (feature 5 > 2.0408593821519982)
Predict: 2.0
Else (feature 4 > 4.0067789684062625)
Predict: 4.0
Else (feature 1 > -9.356527272727273)
Predict: 0.0
Else (feature 2 > -1.8347482272727271)
If (feature 1 <= -3.8209812727272725)
If (feature 1 <= -11.596521363636366)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 5 <= 0.16873060661442005)
Predict: 1.0
Else (feature 5 > 0.16873060661442005)
If (feature 5 <= 3.469288509552827)
Predict: 2.0
Else (feature 5 > 3.469288509552827)
If (feature 4 <= 3.5166543239603483)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 4 <= 3.5166543239603483)
If (feature 2 <= 0.6682646818181818)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 0 > -1.2744209090909089)
If (feature 2 <= 0.6682646818181818)
If (feature 1 <= -3.8209812727272725)
If (feature 5 <= 4.913251092952768)
Predict: 2.0
Else (feature 5 > 4.913251092952768)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 3 <= 2.696622839121874)
Predict: 0.0
Else (feature 3 > 2.696622839121874)
If (feature 0 <= -0.1735843181818182)
Predict: 0.0
Else (feature 0 > -0.1735843181818182)
If (feature 5 <= 0.7848413904200169)
Predict: 4.0
Else (feature 5 > 0.7848413904200169)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
If (feature 1 <= -8.948937727272728)
If (feature 0 <= 0.32806340909090914)
Predict: 0.0
Else (feature 0 > 0.32806340909090914)
Predict: 2.0
Else (feature 1 > -8.948937727272728)
Predict: 0.0
Else (feature 0 > 3.5330349999999995)
If (feature 0 <= 4.459689318181818)
If (feature 1 <= -13.810390454545457)
Predict: 0.0
Else (feature 1 > -13.810390454545457)
If (feature 4 <= 2.891537202575433)
Predict: 2.0
Else (feature 4 > 2.891537202575433)
Predict: 0.0
Else (feature 0 > 4.459689318181818)
Predict: 0.0
Else (feature 4 > 4.527012376192168)
If (feature 4 <= 8.132821942578682)
If (feature 0 <= -3.502226363636364)
If (feature 1 <= -11.596521363636366)
If (feature 2 <= -3.569613363636363)
Predict: 4.0
Else (feature 2 > -3.569613363636363)
If (feature 3 <= 2.9289256343028125)
Predict: 2.0
Else (feature 3 > 2.9289256343028125)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 2 <= 0.6682646818181818)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
If (feature 4 <= 6.684054463098245)
Predict: 0.0
Else (feature 4 > 6.684054463098245)
Predict: 2.0
Else (feature 0 > -3.502226363636364)
If (feature 1 <= -3.8209812727272725)
If (feature 0 <= 3.5330349999999995)
If (feature 5 <= 3.1250203637838814)
If (feature 2 <= -5.720776181818183)
Predict: 2.0
Else (feature 2 > -5.720776181818183)
If (feature 2 <= 1.1333349545454543)
If (feature 1 <= -5.109937500000001)
If (feature 3 <= 1.7795641834853977)
Predict: 4.0
Else (feature 3 > 1.7795641834853977)
If (feature 0 <= 0.9882181363636364)
If (feature 0 <= 0.5266320454545453)
If (feature 2 <= -1.2442670454545455)
If (feature 4 <= 5.398982323543355)
Predict: 0.0
Else (feature 4 > 5.398982323543355)
If (feature 5 <= 2.8353340431585474)
Predict: 4.0
Else (feature 5 > 2.8353340431585474)
If (feature 0 <= -1.4538298181818181)
If (feature 1 <= -9.49238863636364)
Predict: 0.0
Else (feature 1 > -9.49238863636364)
Predict: 4.0
Else (feature 0 > -1.4538298181818181)
Predict: 4.0
Else (feature 2 > -1.2442670454545455)
Predict: 0.0
Else (feature 0 > 0.5266320454545453)
Predict: 0.0
Else (feature 0 > 0.9882181363636364)
If (feature 4 <= 6.291886217849433)
Predict: 0.0
Else (feature 4 > 6.291886217849433)
If (feature 1 <= -5.750931181818182)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
If (feature 3 <= 2.283530423945612)
Predict: 0.0
Else (feature 3 > 2.283530423945612)
If (feature 3 <= 2.696622839121874)
If (feature 3 <= 2.4887269112897967)
Predict: 4.0
Else (feature 3 > 2.4887269112897967)
Predict: 0.0
Else (feature 3 > 2.696622839121874)
Predict: 4.0
Else (feature 1 > -5.109937500000001)
If (feature 4 <= 5.019055660088064)
Predict: 0.0
Else (feature 4 > 5.019055660088064)
If (feature 0 <= -3.065027181818182)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
If (feature 4 <= 7.3665835405207885)
Predict: 4.0
Else (feature 4 > 7.3665835405207885)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
If (feature 3 <= 1.355530712657193)
Predict: 2.0
Else (feature 3 > 1.355530712657193)
If (feature 5 <= 1.6130554927490437)
Predict: 0.0
Else (feature 5 > 1.6130554927490437)
If (feature 0 <= -0.3791205454545455)
If (feature 4 <= 5.398982323543355)
Predict: 0.0
Else (feature 4 > 5.398982323543355)
Predict: 2.0
Else (feature 0 > -0.3791205454545455)
Predict: 4.0
Else (feature 5 > 3.1250203637838814)
If (feature 2 <= -1.8347482272727271)
If (feature 1 <= -8.4067287012987)
If (feature 3 <= 2.283530423945612)
If (feature 0 <= 0.45695872727272724)
If (feature 3 <= 2.037322122633077)
If (feature 4 <= 5.019055660088064)
Predict: 4.0
Else (feature 4 > 5.019055660088064)
Predict: 0.0
Else (feature 3 > 2.037322122633077)
Predict: 2.0
Else (feature 0 > 0.45695872727272724)
Predict: 4.0
Else (feature 3 > 2.283530423945612)
Predict: 0.0
Else (feature 1 > -8.4067287012987)
If (feature 5 <= 8.699793726565169)
If (feature 1 <= -6.820416818181818)
If (feature 4 <= 7.033163268276285)
Predict: 4.0
Else (feature 4 > 7.033163268276285)
If (feature 3 <= 2.4887269112897967)
Predict: 0.0
Else (feature 3 > 2.4887269112897967)
Predict: 4.0
Else (feature 1 > -6.820416818181818)
Predict: 4.0
Else (feature 5 > 8.699793726565169)
Predict: 0.0
Else (feature 2 > -1.8347482272727271)
If (feature 5 <= 3.8343025836297375)
If (feature 1 <= -8.097179727272728)
Predict: 2.0
Else (feature 1 > -8.097179727272728)
If (feature 3 <= 2.9289256343028125)
If (feature 3 <= 2.4887269112897967)
If (feature 0 <= -3.065027181818182)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
Predict: 0.0
Else (feature 3 > 2.4887269112897967)
Predict: 0.0
Else (feature 3 > 2.9289256343028125)
Predict: 4.0
Else (feature 5 > 3.8343025836297375)
If (feature 2 <= -0.9551237727272728)
If (feature 5 <= 6.108758959317355)
Predict: 4.0
Else (feature 5 > 6.108758959317355)
Predict: 0.0
Else (feature 2 > -0.9551237727272728)
Predict: 0.0
Else (feature 0 > 3.5330349999999995)
If (feature 4 <= 7.72195028414196)
Predict: 0.0
Else (feature 4 > 7.72195028414196)
If (feature 1 <= -8.712045)
Predict: 0.0
Else (feature 1 > -8.712045)
Predict: 4.0
Else (feature 1 > -3.8209812727272725)
If (feature 0 <= -3.065027181818182)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
If (feature 1 <= -2.2306882272727275)
If (feature 3 <= 2.037322122633077)
If (feature 5 <= 1.9145738226248412)
Predict: 4.0
Else (feature 5 > 1.9145738226248412)
Predict: 0.0
Else (feature 3 > 2.037322122633077)
Predict: 0.0
Else (feature 1 > -2.2306882272727275)
Predict: 0.0
Else (feature 4 > 8.132821942578682)
If (feature 4 <= 8.97158739539847)
If (feature 2 <= -1.2442670454545455)
If (feature 1 <= -3.8209812727272725)
If (feature 2 <= -5.720776181818183)
Predict: 0.0
Else (feature 2 > -5.720776181818183)
If (feature 0 <= 0.43257299999999993)
Predict: 4.0
Else (feature 0 > 0.43257299999999993)
If (feature 2 <= -2.810174181818182)
Predict: 4.0
Else (feature 2 > -2.810174181818182)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
Predict: 0.0
Else (feature 2 > -1.2442670454545455)
If (feature 5 <= 1.9145738226248412)
If (feature 2 <= 0.1387476818181818)
Predict: 0.0
Else (feature 2 > 0.1387476818181818)
If (feature 0 <= -0.7518725909090909)
Predict: 0.0
Else (feature 0 > -0.7518725909090909)
Predict: 4.0
Else (feature 5 > 1.9145738226248412)
Predict: 0.0
Else (feature 4 > 8.97158739539847)
Predict: 0.0
Else (feature 3 > 3.2305826019123103)
If (feature 1 <= -10.088093363636364)
If (feature 3 <= 5.42131601555171)
If (feature 0 <= 2.0089040454545453)
If (feature 5 <= 4.262646949929151)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 4 <= 4.527012376192168)
If (feature 0 <= -0.7518725909090909)
If (feature 5 <= 1.2559600735949519)
Predict: 0.0
Else (feature 5 > 1.2559600735949519)
Predict: 2.0
Else (feature 0 > -0.7518725909090909)
Predict: 0.0
Else (feature 4 > 4.527012376192168)
If (feature 4 <= 6.684054463098245)
Predict: 0.0
Else (feature 4 > 6.684054463098245)
If (feature 0 <= 0.32806340909090914)
If (feature 4 <= 7.3665835405207885)
If (feature 3 <= 4.596493775946534)
Predict: 2.0
Else (feature 3 > 4.596493775946534)
Predict: 0.0
Else (feature 4 > 7.3665835405207885)
Predict: 0.0
Else (feature 0 > 0.32806340909090914)
Predict: 2.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
Predict: 0.0
Else (feature 3 > 5.42131601555171)
Predict: 0.0
Else (feature 1 > -10.088093363636364)
If (feature 5 <= 1.0680362943269028)
If (feature 4 <= 5.830630767197693)
*** WARNING: skipped 325736 bytes of output ***
Predict: 2.0
Else (feature 4 > 5.398982323543355)
If (feature 0 <= -2.5093826818181824)
Predict: 4.0
Else (feature 0 > -2.5093826818181824)
Predict: 2.0
Else (feature 1 > -7.698300909090908)
If (feature 2 <= -1.073569090909091)
If (feature 3 <= 2.696622839121874)
If (feature 1 <= -7.295937636363637)
If (feature 3 <= 1.7795641834853977)
Predict: 4.0
Else (feature 3 > 1.7795641834853977)
Predict: 0.0
Else (feature 1 > -7.295937636363637)
If (feature 4 <= 4.527012376192168)
Predict: 2.0
Else (feature 4 > 4.527012376192168)
Predict: 0.0
Else (feature 3 > 2.696622839121874)
Predict: 0.0
Else (feature 2 > -1.073569090909091)
Predict: 2.0
Else (feature 1 > -5.750931181818182)
If (feature 3 <= 5.42131601555171)
If (feature 2 <= 6.177680772727272)
If (feature 1 <= -2.2306882272727275)
If (feature 3 <= 1.7795641834853977)
Predict: 4.0
Else (feature 3 > 1.7795641834853977)
If (feature 5 <= 1.2559600735949519)
If (feature 4 <= 2.891537202575433)
Predict: 2.0
Else (feature 4 > 2.891537202575433)
Predict: 0.0
Else (feature 5 > 1.2559600735949519)
If (feature 0 <= 2.0089040454545453)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
If (feature 4 <= 2.891537202575433)
Predict: 0.0
Else (feature 4 > 2.891537202575433)
Predict: 4.0
Else (feature 1 > -2.2306882272727275)
If (feature 4 <= 3.5166543239603483)
If (feature 0 <= 0.9882181363636364)
Predict: 0.0
Else (feature 0 > 0.9882181363636364)
If (feature 5 <= 0.7848413904200169)
Predict: 4.0
Else (feature 5 > 0.7848413904200169)
Predict: 0.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 2 > 6.177680772727272)
Predict: 2.0
Else (feature 3 > 5.42131601555171)
If (feature 4 <= 5.019055660088064)
Predict: 0.0
Else (feature 4 > 5.019055660088064)
If (feature 3 <= 7.291873594931593)
If (feature 1 <= -5.109937500000001)
Predict: 0.0
Else (feature 1 > -5.109937500000001)
If (feature 1 <= -2.2306882272727275)
Predict: 4.0
Else (feature 1 > -2.2306882272727275)
Predict: 0.0
Else (feature 3 > 7.291873594931593)
Predict: 0.0
Else (feature 4 > 5.830630767197693)
If (feature 0 <= -3.065027181818182)
If (feature 4 <= 7.3665835405207885)
If (feature 5 <= 1.4557098485258626)
If (feature 3 <= 3.5937950153207483)
If (feature 4 <= 6.291886217849433)
If (feature 0 <= -3.502226363636364)
Predict: 0.0
Else (feature 0 > -3.502226363636364)
Predict: 4.0
Else (feature 4 > 6.291886217849433)
Predict: 0.0
Else (feature 3 > 3.5937950153207483)
If (feature 2 <= 0.24674104545454545)
Predict: 4.0
Else (feature 2 > 0.24674104545454545)
If (feature 3 <= 5.42131601555171)
Predict: 4.0
Else (feature 3 > 5.42131601555171)
Predict: 0.0
Else (feature 5 > 1.4557098485258626)
If (feature 2 <= 0.1387476818181818)
Predict: 0.0
Else (feature 2 > 0.1387476818181818)
If (feature 3 <= 1.0884225728208126)
Predict: 2.0
Else (feature 3 > 1.0884225728208126)
Predict: 4.0
Else (feature 4 > 7.3665835405207885)
If (feature 3 <= 4.092746328718364)
Predict: 0.0
Else (feature 3 > 4.092746328718364)
Predict: 4.0
Else (feature 0 > -3.065027181818182)
If (feature 1 <= -9.433167272727271)
If (feature 2 <= -3.569613363636363)
If (feature 3 <= 2.037322122633077)
Predict: 4.0
Else (feature 3 > 2.037322122633077)
Predict: 2.0
Else (feature 2 > -3.569613363636363)
If (feature 0 <= -1.976380909090909)
If (feature 0 <= -2.5093826818181824)
Predict: 4.0
Else (feature 0 > -2.5093826818181824)
Predict: 2.0
Else (feature 0 > -1.976380909090909)
Predict: 0.0
Else (feature 1 > -9.433167272727271)
If (feature 3 <= 8.670695907958262)
If (feature 2 <= 1.1333349545454543)
If (feature 1 <= -1.1942972727272725)
If (feature 4 <= 8.97158739539847)
If (feature 2 <= -0.6624949545454546)
If (feature 0 <= 7.344164590909091)
If (feature 2 <= -5.720776181818183)
Predict: 0.0
Else (feature 2 > -5.720776181818183)
If (feature 1 <= -2.2306882272727275)
If (feature 1 <= -5.750931181818182)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
If (feature 3 <= 2.9289256343028125)
If (feature 0 <= 1.2895552272727273)
Predict: 4.0
Else (feature 0 > 1.2895552272727273)
Predict: 0.0
Else (feature 3 > 2.9289256343028125)
Predict: 4.0
Else (feature 1 > -2.2306882272727275)
If (feature 5 <= 2.179859408203087)
Predict: 4.0
Else (feature 5 > 2.179859408203087)
Predict: 0.0
Else (feature 0 > 7.344164590909091)
If (feature 5 <= 1.7614806708061252)
Predict: 4.0
Else (feature 5 > 1.7614806708061252)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
If (feature 4 <= 7.033163268276285)
If (feature 2 <= -0.5126976818181816)
Predict: 4.0
Else (feature 2 > -0.5126976818181816)
If (feature 1 <= -3.8209812727272725)
If (feature 2 <= 0.29899645454545454)
If (feature 5 <= 1.7614806708061252)
Predict: 0.0
Else (feature 5 > 1.7614806708061252)
Predict: 4.0
Else (feature 2 > 0.29899645454545454)
Predict: 4.0
Else (feature 1 > -3.8209812727272725)
Predict: 0.0
Else (feature 4 > 7.033163268276285)
If (feature 5 <= 2.0408593821519982)
If (feature 1 <= -5.109937500000001)
If (feature 3 <= 1.5843984037196344)
Predict: 0.0
Else (feature 3 > 1.5843984037196344)
Predict: 4.0
Else (feature 1 > -5.109937500000001)
Predict: 0.0
Else (feature 5 > 2.0408593821519982)
If (feature 0 <= 3.5330349999999995)
If (feature 1 <= -5.750931181818182)
If (feature 3 <= 2.037322122633077)
Predict: 0.0
Else (feature 3 > 2.037322122633077)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
Predict: 0.0
Else (feature 0 > 3.5330349999999995)
If (feature 5 <= 2.179859408203087)
Predict: 4.0
Else (feature 5 > 2.179859408203087)
Predict: 0.0
Else (feature 4 > 8.97158739539847)
Predict: 0.0
Else (feature 1 > -1.1942972727272725)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
If (feature 1 <= -5.750931181818182)
If (feature 0 <= 0.9882181363636364)
If (feature 5 <= 1.7614806708061252)
Predict: 0.0
Else (feature 5 > 1.7614806708061252)
Predict: 2.0
Else (feature 0 > 0.9882181363636364)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
Predict: 0.0
Else (feature 3 > 8.670695907958262)
Predict: 0.0
Else (feature 5 > 2.600144428803959)
If (feature 3 <= 3.2305826019123103)
If (feature 0 <= 2.0089040454545453)
If (feature 1 <= -7.698300909090908)
If (feature 5 <= 6.108758959317355)
If (feature 1 <= -11.596521363636366)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 0 <= -1.976380909090909)
If (feature 3 <= 2.9289256343028125)
If (feature 5 <= 3.8343025836297375)
Predict: 4.0
Else (feature 5 > 3.8343025836297375)
Predict: 2.0
Else (feature 3 > 2.9289256343028125)
Predict: 0.0
Else (feature 0 > -1.976380909090909)
If (feature 4 <= 7.033163268276285)
Predict: 2.0
Else (feature 4 > 7.033163268276285)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 2 <= -2.4757429090909095)
If (feature 3 <= 2.037322122633077)
Predict: 4.0
Else (feature 3 > 2.037322122633077)
If (feature 4 <= 6.291886217849433)
Predict: 2.0
Else (feature 4 > 6.291886217849433)
Predict: 0.0
Else (feature 2 > -2.4757429090909095)
If (feature 4 <= 6.684054463098245)
Predict: 2.0
Else (feature 4 > 6.684054463098245)
If (feature 1 <= -10.088093363636364)
Predict: 2.0
Else (feature 1 > -10.088093363636364)
If (feature 0 <= -4.214637727272727)
Predict: 2.0
Else (feature 0 > -4.214637727272727)
Predict: 0.0
Else (feature 5 > 6.108758959317355)
If (feature 1 <= -9.276403181818182)
Predict: 0.0
Else (feature 1 > -9.276403181818182)
If (feature 2 <= -3.569613363636363)
Predict: 4.0
Else (feature 2 > -3.569613363636363)
Predict: 0.0
Else (feature 1 > -7.698300909090908)
If (feature 4 <= 6.684054463098245)
If (feature 4 <= 3.5166543239603483)
If (feature 1 <= -5.109937500000001)
Predict: 2.0
Else (feature 1 > -5.109937500000001)
If (feature 2 <= 1.1333349545454543)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
If (feature 3 <= 1.7795641834853977)
If (feature 5 <= 3.469288509552827)
If (feature 0 <= -3.065027181818182)
If (feature 1 <= -3.8209812727272725)
Predict: 2.0
Else (feature 1 > -3.8209812727272725)
Predict: 0.0
Else (feature 0 > -3.065027181818182)
Predict: 4.0
Else (feature 5 > 3.469288509552827)
Predict: 0.0
Else (feature 3 > 1.7795641834853977)
If (feature 1 <= -7.295937636363637)
Predict: 2.0
Else (feature 1 > -7.295937636363637)
If (feature 4 <= 4.0067789684062625)
If (feature 0 <= -1.105464818181818)
Predict: 0.0
Else (feature 0 > -1.105464818181818)
Predict: 2.0
Else (feature 4 > 4.0067789684062625)
Predict: 0.0
Else (feature 4 > 6.684054463098245)
If (feature 1 <= -3.8209812727272725)
If (feature 5 <= 8.699793726565169)
If (feature 3 <= 1.5843984037196344)
Predict: 0.0
Else (feature 3 > 1.5843984037196344)
If (feature 2 <= -1.073569090909091)
If (feature 5 <= 4.262646949929151)
Predict: 4.0
Else (feature 5 > 4.262646949929151)
If (feature 0 <= 0.9882181363636364)
If (feature 2 <= -2.198210545454545)
Predict: 4.0
Else (feature 2 > -2.198210545454545)
If (feature 0 <= 0.43257299999999993)
Predict: 4.0
Else (feature 0 > 0.43257299999999993)
Predict: 0.0
Else (feature 0 > 0.9882181363636364)
Predict: 4.0
Else (feature 2 > -1.073569090909091)
If (feature 3 <= 2.4887269112897967)
Predict: 2.0
Else (feature 3 > 2.4887269112897967)
Predict: 0.0
Else (feature 5 > 8.699793726565169)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 5 <= 3.469288509552827)
Predict: 2.0
Else (feature 5 > 3.469288509552827)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
If (feature 5 <= 2.8353340431585474)
If (feature 2 <= -5.720776181818183)
Predict: 2.0
Else (feature 2 > -5.720776181818183)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 4 <= 4.0067789684062625)
If (feature 3 <= 2.283530423945612)
If (feature 1 <= -13.810390454545457)
Predict: 0.0
Else (feature 1 > -13.810390454545457)
If (feature 2 <= -1.2442670454545455)
Predict: 2.0
Else (feature 2 > -1.2442670454545455)
Predict: 0.0
Else (feature 3 > 2.283530423945612)
Predict: 0.0
Else (feature 4 > 4.0067789684062625)
If (feature 5 <= 3.1250203637838814)
If (feature 3 <= 2.9289256343028125)
Predict: 0.0
Else (feature 3 > 2.9289256343028125)
If (feature 0 <= 2.8563333333333336)
Predict: 4.0
Else (feature 0 > 2.8563333333333336)
Predict: 0.0
Else (feature 5 > 3.1250203637838814)
Predict: 0.0
Else (feature 3 > 3.2305826019123103)
If (feature 3 <= 4.596493775946534)
If (feature 1 <= -8.097179727272728)
If (feature 5 <= 3.469288509552827)
If (feature 3 <= 3.5937950153207483)
If (feature 5 <= 2.8353340431585474)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 0 <= 2.0089040454545453)
If (feature 4 <= 5.398982323543355)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 4 > 5.398982323543355)
Predict: 2.0
Else (feature 0 > 2.0089040454545453)
Predict: 0.0
Else (feature 3 > 3.5937950153207483)
If (feature 2 <= -0.6624949545454546)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
If (feature 0 <= 2.0089040454545453)
If (feature 4 <= 7.3665835405207885)
If (feature 5 <= 3.1250203637838814)
Predict: 2.0
Else (feature 5 > 3.1250203637838814)
Predict: 0.0
Else (feature 4 > 7.3665835405207885)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
Predict: 0.0
Else (feature 5 > 3.469288509552827)
If (feature 3 <= 4.092746328718364)
Predict: 0.0
Else (feature 3 > 4.092746328718364)
If (feature 4 <= 7.72195028414196)
Predict: 0.0
Else (feature 4 > 7.72195028414196)
If (feature 1 <= -11.596521363636366)
If (feature 5 <= 4.262646949929151)
Predict: 2.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 1 <= -8.4067287012987)
Predict: 0.0
Else (feature 1 > -8.4067287012987)
If (feature 0 <= 1.2895552272727273)
Predict: 0.0
Else (feature 0 > 1.2895552272727273)
Predict: 4.0
Else (feature 1 > -8.097179727272728)
If (feature 1 <= -3.8209812727272725)
If (feature 2 <= -1.5194759545454544)
If (feature 4 <= 5.830630767197693)
Predict: 0.0
Else (feature 4 > 5.830630767197693)
If (feature 5 <= 6.108758959317355)
If (feature 2 <= -5.720776181818183)
Predict: 0.0
Else (feature 2 > -5.720776181818183)
If (feature 0 <= -0.7518725909090909)
Predict: 0.0
Else (feature 0 > -0.7518725909090909)
Predict: 4.0
Else (feature 5 > 6.108758959317355)
Predict: 0.0
Else (feature 2 > -1.5194759545454544)
If (feature 2 <= -0.18174913636363638)
If (feature 1 <= -5.750931181818182)
Predict: 0.0
Else (feature 1 > -5.750931181818182)
If (feature 0 <= -7.121753363636364)
Predict: 0.0
Else (feature 0 > -7.121753363636364)
Predict: 4.0
Else (feature 2 > -0.18174913636363638)
If (feature 0 <= 2.0089040454545453)
If (feature 2 <= 1.1333349545454543)
If (feature 0 <= -1.976380909090909)
If (feature 0 <= -4.165866818181819)
Predict: 2.0
Else (feature 0 > -4.165866818181819)
Predict: 0.0
Else (feature 0 > -1.976380909090909)
Predict: 2.0
Else (feature 2 > 1.1333349545454543)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
If (feature 0 <= 4.459689318181818)
Predict: 4.0
Else (feature 0 > 4.459689318181818)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 2 <= 0.6682646818181818)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
If (feature 4 <= 3.5166543239603483)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 3 > 4.596493775946534)
If (feature 4 <= 6.291886217849433)
If (feature 2 <= -0.6624949545454546)
If (feature 5 <= 2.8353340431585474)
If (feature 0 <= 0.43257299999999993)
Predict: 0.0
Else (feature 0 > 0.43257299999999993)
If (feature 3 <= 8.670695907958262)
If (feature 3 <= 6.490709433033732)
Predict: 4.0
Else (feature 3 > 6.490709433033732)
If (feature 3 <= 7.291873594931593)
Predict: 0.0
Else (feature 3 > 7.291873594931593)
Predict: 4.0
Else (feature 3 > 8.670695907958262)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 1 <= -6.820416818181818)
If (feature 1 <= -7.295937636363637)
Predict: 0.0
Else (feature 1 > -7.295937636363637)
If (feature 2 <= -1.8347482272727271)
Predict: 0.0
Else (feature 2 > -1.8347482272727271)
If (feature 2 <= -1.073569090909091)
Predict: 2.0
Else (feature 2 > -1.073569090909091)
Predict: 0.0
Else (feature 1 > -6.820416818181818)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
If (feature 1 <= -8.948937727272728)
Predict: 0.0
Else (feature 1 > -8.948937727272728)
If (feature 3 <= 5.42131601555171)
If (feature 0 <= 0.32806340909090914)
If (feature 1 <= -6.2386456818181815)
If (feature 0 <= -3.065027181818182)
Predict: 0.0
Else (feature 0 > -3.065027181818182)
If (feature 5 <= 4.262646949929151)
Predict: 2.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 1 > -6.2386456818181815)
Predict: 2.0
Else (feature 0 > 0.32806340909090914)
Predict: 0.0
Else (feature 3 > 5.42131601555171)
If (feature 2 <= -0.5126976818181816)
If (feature 3 <= 5.87583214563006)
If (feature 4 <= 5.019055660088064)
Predict: 0.0
Else (feature 4 > 5.019055660088064)
Predict: 2.0
Else (feature 3 > 5.87583214563006)
Predict: 0.0
Else (feature 2 > -0.5126976818181816)
Predict: 0.0
Else (feature 4 > 6.291886217849433)
If (feature 5 <= 2.8353340431585474)
If (feature 4 <= 7.72195028414196)
If (feature 0 <= 5.969858363636364)
If (feature 0 <= -7.121753363636364)
Predict: 0.0
Else (feature 0 > -7.121753363636364)
Predict: 4.0
Else (feature 0 > 5.969858363636364)
Predict: 0.0
Else (feature 4 > 7.72195028414196)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 3 <= 5.42131601555171)
If (feature 2 <= -1.073569090909091)
If (feature 5 <= 4.913251092952768)
If (feature 0 <= 2.8563333333333336)
If (feature 4 <= 7.3665835405207885)
Predict: 0.0
Else (feature 4 > 7.3665835405207885)
If (feature 0 <= -5.695193636363635)
Predict: 2.0
Else (feature 0 > -5.695193636363635)
Predict: 4.0
Else (feature 0 > 2.8563333333333336)
Predict: 0.0
Else (feature 5 > 4.913251092952768)
Predict: 0.0
Else (feature 2 > -1.073569090909091)
If (feature 5 <= 4.262646949929151)
If (feature 2 <= -0.18174913636363638)
If (feature 5 <= 3.8343025836297375)
Predict: 0.0
Else (feature 5 > 3.8343025836297375)
Predict: 2.0
Else (feature 2 > -0.18174913636363638)
Predict: 0.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 3 > 5.42131601555171)
If (feature 1 <= -8.948937727272728)
Predict: 0.0
Else (feature 1 > -8.948937727272728)
If (feature 1 <= -8.712045)
If (feature 3 <= 5.87583214563006)
Predict: 4.0
Else (feature 3 > 5.87583214563006)
Predict: 0.0
Else (feature 1 > -8.712045)
If (feature 2 <= -0.6624949545454546)
If (feature 5 <= 3.1250203637838814)
If (feature 3 <= 8.670695907958262)
If (feature 3 <= 6.490709433033732)
Predict: 4.0
Else (feature 3 > 6.490709433033732)
If (feature 4 <= 7.033163268276285)
Predict: 0.0
Else (feature 4 > 7.033163268276285)
If (feature 2 <= -1.0073777272727273)
If (feature 1 <= -5.109937500000001)
Predict: 4.0
Else (feature 1 > -5.109937500000001)
Predict: 0.0
Else (feature 2 > -1.0073777272727273)
Predict: 0.0
Else (feature 3 > 8.670695907958262)
Predict: 0.0
Else (feature 5 > 3.1250203637838814)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
Predict: 0.0
import org.apache.spark.ml.classification.RandomForestClassificationModel
rfModel: org.apache.spark.ml.classification.RandomForestClassificationModel = RandomForestClassificationModel: uid=rfc_6ab6b8d21734, numTrees=10, numClasses=5, numFeatures=6
Save and Load later to Serve Predictions
When you are satisfied with the model you have trained/fitted then you can save it to a file and load it again from another low-latency prediciton-serving system to serve predictions.
model
res24: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.PipelineModel
Save the model to a file in stable storage.
model.write.overwrite().save("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")
Load the model, typically from a prediction or model serving layer/system.
val same_rfModelFromSavedFile = PipelineModel.load("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")
same_rfModelFromSavedFile: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
same_rfModelFromSavedFile
res26: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
Suppose new data is coming at us and we need to serve our predicitons of the activities
Note: We only need accelerometer readings so prediciton pipeline can be simplified to only take in the needed features for prediction. For simplicity let's assume the test data is representative of the new data (previously unseen) that is coming at us for serving our activity predicitons.
val newDataForPrediction = testData.sample(0.0004,12348L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|
| -1.9955399999999999 | -9.258980909090907 | 1.0410163636363636 | 1.0549255357138732 | 0.7680301427703919 | 1.8853035398286844 |
Here is another new data coming at you... you can predict the current activity being done by simply transforming the loaded model (estimator).
val newDataForPrediction = testData.sample(0.0004,1234L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|
| 0.42212218181818173 | -0.7135524545454545 | 9.440134545454546 | 8.409472091286836e-2 | 9.073949633706861e-2 | 9.389382640736067e-2 |
display(same_rfModelFromSavedFile.transform(newDataForPrediction).select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction"))
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ | prediction |
|---|---|---|---|---|---|---|
| 0.42212218181818173 | -0.7135524545454545 | 9.440134545454546 | 8.409472091286836e-2 | 9.073949633706861e-2 | 9.389382640736067e-2 | 3.0 |
Some minor post-processing to connect prediction label to the activity string.
Typically one prediction is just a small part of larger business or science use-case.
activityLabelDF.show
+--------+-----+
|activity|label|
+--------+-----+
| Jogging| 0.0|
| Jumping| 4.0|
|Standing| 1.0|
| Walking| 2.0|
| Sitting| 3.0|
+--------+-----+
display(same_rfModelFromSavedFile
.transform(newDataForPrediction)
.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction")
.join(activityLabelDF,$"prediction"===$"label")
.drop("prediction","label")
.withColumnRenamed("activity","Your current activity is likely to be")
)
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ | Your current activity is likely to be |
|---|---|---|---|---|---|---|
| 0.42212218181818173 | -0.7135524545454545 | 9.440134545454546 | 8.409472091286836e-2 | 9.073949633706861e-2 | 9.389382640736067e-2 | Sitting |
An activity-based music-recommendation system - a diatribe:
For example, you may want to correlate historical music-listening habits for a user with their historical physical activities and then recommend music to them using a subsequent recommender system pipeline that uses activity prediciton as one of its input.
NOTE: One has to be legally compliant for using the data in such manner with client consent, depending on the jurisdiction of your operations - GDPR applies for EU operations, for example --- this is coming attraction!
display(same_rfModelFromSavedFile.transform(testData.sample(0.01))) // note more can be done with such data, time of day can be informative for other decision problems
- Download and Load Data
The following anonymized dataset is used with kind permission of Amira Lakhal.
wget http://lamastex.org/datasets/public/ActivityRecognition/dataTraining.csv
--2020-11-13 09:48:32-- http://lamastex.org/datasets/public/ActivityRecognition/dataTraining.csv
Resolving lamastex.org (lamastex.org)... 166.62.28.100
Connecting to lamastex.org (lamastex.org)|166.62.28.100|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 931349 (910K) [text/csv]
Saving to: ‘dataTraining.csv’
0K .......... .......... .......... .......... .......... 5% 140K 6s
50K .......... .......... .......... .......... .......... 10% 282K 4s
100K .......... .......... .......... .......... .......... 16% 288K 4s
150K .......... .......... .......... .......... .......... 21% 14.8M 3s
200K .......... .......... .......... .......... .......... 27% 33.2M 2s
250K .......... .......... .......... .......... .......... 32% 287K 2s
300K .......... .......... .......... .......... .......... 38% 35.0M 1s
350K .......... .......... .......... .......... .......... 43% 35.7M 1s
400K .......... .......... .......... .......... .......... 49% 48.3M 1s
450K .......... .......... .......... .......... .......... 54% 293K 1s
500K .......... .......... .......... .......... .......... 60% 20.8M 1s
550K .......... .......... .......... .......... .......... 65% 44.5M 1s
600K .......... .......... .......... .......... .......... 71% 39.1M 0s
650K .......... .......... .......... .......... .......... 76% 44.6M 0s
700K .......... .......... .......... .......... .......... 82% 41.9M 0s
750K .......... .......... .......... .......... .......... 87% 42.0M 0s
800K .......... .......... .......... .......... .......... 93% 42.4M 0s
850K .......... .......... .......... .......... .......... 98% 297K 0s
900K ......... 100% 35.8M=1.2s
2020-11-13 09:48:33 (734 KB/s) - ‘dataTraining.csv’ saved [931349/931349]
pwd && ls
/databricks/driver
conf
dataTraining.csv
derby.log
eventlogs
ganglia
logs
//dbutils.fs.mkdirs("dbfs:///datasets/sds/ActivityRecognition") // make directory if needed
dbutils.fs.mv("file:///databricks/driver/dataTraining.csv","dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
res25: Boolean = true
display(dbutils.fs.ls("dbfs:///datasets/sds/ActivityRecognition"))
| path | name | size |
|---|---|---|
| dbfs:/datasets/sds/ActivityRecognition/dataTraining.csv | dataTraining.csv | 931349.0 |
dbutils.fs.head("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
[Truncated to first 65536 bytes]
res27: String =
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
"user_001","Jumping",1446047227865,"3.44943","-1.68549","2.29862"
"user_001","Jumping",1446047227930,"1.87829","-1.91542","0.880768"
"user_001","Jumping",1446047227995,"1.57173","-5.86241","-3.75599"
"user_001","Jumping",1446047228059,"3.41111","-17.93331","0.535886"
"user_001","Jumping",1446047228123,"3.18118","-19.58108","5.74745"
"user_001","Jumping",1446047228189,"7.85626","-19.2362","0.804128"
"user_001","Jumping",1446047228253,"1.26517","-8.85139","2.18366"
"user_001","Jumping",1446047228318,"0.077239","1.15021","1.53221"
"user_001","Jumping",1446047228383,"0.230521","2.0699","-1.41845"
"user_001","Jumping",1446047228447,"0.652044","-0.497565","1.76214"
"user_001","Jumping",1446047228512,"1.53341","-0.305964","1.41725"
"user_001","Jumping",1446047228578,"-1.07237","-1.95374","0.191003"
"user_001","Jumping",1446047228642,"2.75966","-13.75639","0.191003"
"user_001","Jumping",1446047228707,"7.43474","-19.58108","2.95007"
"user_001","Jumping",1446047228771,"5.63368","-19.58108","5.59417"
"user_001","Jumping",1446047228836,"5.02056","-11.72542","-1.38013"
"user_001","Jumping",1446047228900,"-2.10702","0.575403","1.07237"
"user_001","Jumping",1446047228966,"-1.30229","2.2615","-1.26517"
"user_001","Jumping",1446047229030,"1.68669","-0.957409","1.57053"
"user_001","Jumping",1446047229095,"2.60638","-0.229323","2.14534"
"user_001","Jumping",1446047229159,"1.30349","-0.152682","0.497565"
"user_001","Jumping",1446047229224,"1.64837","-8.12331","1.60885"
"user_001","Jumping",1446047229290,"0.15388","-18.46979","-1.03525"
"user_001","Jumping",1446047229354,"4.98224","-19.58108","0.995729"
"user_001","Jumping",1446047229419,"5.17384","-19.2362","0.612526"
"user_001","Jumping",1446047229483,"2.03158","-9.11964","1.34061"
"user_001","Jumping",1446047229549,"-1.95374","-1.03405","0.574206"
"user_001","Jumping",1446047229612,"0.1922","1.30349","-1.03525"
"user_001","Jumping",1446047229678,"1.64837","-0.727487","-0.307161"
"user_001","Jumping",1446047229743,"2.56806","-1.03405","0.267643"
"user_001","Jumping",1446047229806,"1.41845","-0.305964","0.727487"
"user_001","Jumping",1446047229872,"1.03525","-6.82042","0.957409"
"user_001","Jumping",1446047229936,"3.94759","-19.31284","-1.22685"
"user_001","Jumping",1446047230001,"6.13185","-19.58108","2.72014"
"user_001","Jumping",1446047230066,"7.89458","-16.9753","0.229323"
"user_001","Jumping",1446047230131,"2.6447","-3.75479","0.114362"
"user_001","Jumping",1446047230195,"-2.41358","1.91661","-0.000599"
"user_001","Jumping",1446047230260,"-1.95374","-0.114362","-0.268841"
"user_001","Jumping",1446047230325,"-0.229323","-1.95374","2.75846"
"user_001","Jumping",1446047230390,"2.68302","0.268841","0.535886"
"user_001","Jumping",1446047230455,"1.15021","-1.41725","0.880768"
"user_001","Jumping",1446047230519,"2.98958","-11.53382","-0.920286"
"user_001","Jumping",1446047230584,"8.00954","-19.58108","-0.1922"
"user_001","Jumping",1446047230649,"7.31978","-19.58108","2.52854"
"user_001","Jumping",1446047230713,"5.44208","-13.56479","-0.498763"
"user_001","Jumping",1446047230779,"0.728685","-0.420925","1.68549"
"user_001","Jumping",1446047230842,"1.18853","1.41845","-2.22318"
"user_001","Jumping",1446047230907,"0.15388","-1.80046","2.03038"
"user_001","Jumping",1446047230972,"-0.037722","1.07357","-0.958607"
"user_001","Jumping",1446047231038,"0.230521","0.728685","-0.690364"
"user_001","Jumping",1446047231102,"0.805325","-1.14901","1.53221"
"user_001","Jumping",1446047231167,"5.17384","-9.15796","-2.91294"
"user_001","Jumping",1446047231231,"8.00954","-19.54276","1.49389"
"user_001","Jumping",1446047231296,"11.61165","-19.58108","4.25296"
"user_001","Jumping",1446047231361,"7.89458","-18.35483","3.71647"
"user_001","Jumping",1446047231426,"0.537083","-3.21831","0.420925"
"user_001","Jumping",1446047231490,"-1.91542","4.10087","-2.6447"
"user_001","Jumping",1446047231555,"-0.919089","-0.382604","0.114362"
"user_001","Jumping",1446047231620,"0.613724","-0.842448","1.57053"
"user_001","Jumping",1446047231684,"1.38013","-0.459245","0.305964"
"user_001","Jumping",1446047231749,"2.29982","-1.49389","-1.26517"
"user_001","Jumping",1446047231814,"4.94392","-10.95901","-0.498763"
"user_001","Jumping",1446047231878,"3.75599","-19.27452","-1.76333"
"user_001","Jumping",1446047231944,"7.70298","-19.58108","-2.95126"
"user_001","Jumping",1446047232008,"6.55337","-17.51178","-1.91661"
"user_001","Jumping",1446047232073,"2.10822","-2.03038","-0.268841"
"user_001","Jumping",1446047232138,"-1.68549","3.67935","-0.881966"
"user_001","Jumping",1446047232203,"0.15388","-0.114362","0.076042"
"user_001","Jumping",1446047232267,"2.79798","-1.95374","0.689167"
"user_001","Jumping",1446047232332,"1.45677","-0.957409","0.420925"
"user_001","Jumping",1446047232397,"1.18853","-2.95007","0.650847"
"user_001","Jumping",1446047232462,"3.44943","-13.87135","-3.71767"
"user_001","Jumping",1446047232526,"3.79431","-19.58108","-0.345482"
"user_001","Jumping",1446047232591,"6.09353","-19.58108","2.87342"
"user_001","Jumping",1446047232655,"4.48408","-14.3312","-0.498763"
"user_001","Jumping",1446047232720,"-0.037722","-1.68549","2.6435"
"user_001","Jumping",1446047232785,"0.613724","2.68302","-2.37646"
"user_001","Jumping",1446047232850,"0.996927","-0.497565","0.497565"
"user_001","Jumping",1446047232915,"0.15388","-0.689167","1.34061"
"user_001","Jumping",1446047232979,"1.07357","1.07357","-2.60638"
"user_001","Jumping",1446047233044,"2.37646","-3.29495","-1.45677"
"user_001","Jumping",1446047233109,"2.22318","-16.09393","1.37893"
"user_001","Jumping",1446047233173,"6.82161","-19.58108","0.037722"
"user_001","Jumping",1446047233238,"8.39275","-19.54276","1.22565"
"user_001","Jumping",1446047233303,"1.22685","-9.50284","-0.307161"
"user_001","Jumping",1446047233368,"1.83997","-0.152682","1.41725"
"user_001","Jumping",1446047233432,"-0.765807","0.996927","-1.91661"
"user_001","Jumping",1446047233497,"0.728685","-0.612526","0.152682"
"user_001","Jumping",1446047233562,"0.996927","-0.305964","0.995729"
"user_001","Jumping",1446047233626,"1.22685","-0.305964","0.037722"
"user_001","Jumping",1446047233692,"1.91661","-2.95007","-0.345482"
"user_001","Jumping",1446047233755,"4.75232","-14.63776","-3.67935"
"user_001","Jumping",1446047233821,"4.56072","-19.58108","-2.6447"
"user_001","Jumping",1446047233886,"10.69197","-19.38948","-5.94025"
"user_001","Jumping",1446047233950,"3.10454","-10.42253","-2.22318"
"user_001","Jumping",1446047234015,"-0.152682","1.61005","1.80046"
"user_001","Jumping",1446047234080,"-0.267643","1.83997","-0.958607"
"user_001","Jumping",1446047234145,"1.99325","-0.842448","-0.230521"
"user_001","Jumping",1446047234210,"2.79798","-0.114362","0.919089"
"user_001","Jumping",1446047234273,"1.11189","-0.152682","1.83878"
"user_001","Jumping",1446047234339,"2.75966","-5.05768","-0.537083"
"user_001","Jumping",1446047234403,"4.33079","-16.82202","-3.06622"
"user_001","Jumping",1446047234468,"6.89825","-19.58108","-4.25415"
"user_001","Jumping",1446047234533,"11.49669","-19.50444","-2.52974"
"user_001","Jumping",1446047234598,"6.20849","-9.08132","-1.80165"
"user_001","Jumping",1446047234663,"0.805325","1.15021","-0.15388"
"user_001","Jumping",1446047234726,"-1.76214","2.14654","-0.460442"
"user_001","Jumping",1446047234792,"0.000599","-1.11069","-0.230521"
"user_001","Jumping",1446047234857,"0.652044","0.230521","-0.920286"
"user_001","Jumping",1446047234922,"1.26517","-0.114362","-0.000599"
"user_001","Jumping",1446047234986,"5.36544","-4.09967","-0.537083"
"user_001","Jumping",1446047235051,"6.51505","-17.05194","-3.56439"
"user_001","Jumping",1446047235115,"6.93658","-19.58108","-2.75966"
"user_001","Jumping",1446047235181,"7.89458","-19.58108","-3.18118"
"user_001","Jumping",1446047235245,"1.95493","-9.6178","-1.34181"
"user_001","Jumping",1446047235310,"-2.18366","3.14286","-0.1922"
"user_001","Jumping",1446047235375,"-2.87342","1.99325","-1.76333"
"user_001","Jumping",1446047235439,"1.03525","-1.80046","1.64717"
"user_001","Jumping",1446047235504,"1.95493","-0.689167","1.49389"
"user_001","Jumping",1446047235569,"1.26517","0.383802","-0.307161"
"user_001","Jumping",1446047235633,"1.18853","-2.72014","1.41725"
"user_001","Jumping",1446047235698,"5.71033","-16.51545","-2.03158"
"user_001","Jumping",1446047235763,"6.55337","-19.58108","0.995729"
"user_001","Jumping",1446047235828,"10.577","-19.4278","-1.57173"
"user_001","Jumping",1446047235892,"2.79798","-9.88604","-0.843646"
"user_001","Jumping",1446047235957,"0.652044","-0.037722","1.14901"
"user_001","Jumping",1446047236022,"0.843646","1.95493","-2.14654"
"user_001","Jumping",1446047236086,"0.15388","-0.305964","0.995729"
"user_001","Jumping",1446047236152,"0.767005","-0.114362","0.804128"
"user_001","Jumping",1446047236215,"-0.650847","-0.650847","-0.230521"
"user_001","Jumping",1446047236281,"3.56439","-5.90073","0.727487"
"user_001","Jumping",1446047236346,"7.05154","-17.97163","-1.61005"
"user_001","Jumping",1446047236410,"9.46572","-19.58108","2.10702"
"user_001","Jumping",1446047236475,"7.58802","-18.54643","3.10335"
"user_001","Jumping",1446047236540,"0.767005","-6.28393","0.689167"
"user_001","Jumping",1446047236604,"0.307161","2.29982","-0.498763"
"user_001","Jumping",1446047236669,"0.11556","-0.114362","-1.11189"
"user_001","Jumping",1446047236734,"2.2615","-1.45557","2.14534"
"user_001","Jumping",1446047236798,"1.80165","0.345482","0.765807"
"user_001","Jumping",1446047236863,"-0.650847","-0.305964","-1.15021"
"user_001","Jumping",1446047236928,"3.48775","-7.31858","-0.728685"
"user_001","Jumping",1446047236992,"5.32712","-18.92964","0.114362"
"user_001","Jumping",1446047237057,"10.34708","-19.58108","2.37526"
"user_001","Jumping",1446047237122,"5.97857","-17.78002","0.344284"
"user_001","Jumping",1446047237187,"2.8363","-3.25663","0.152682"
"user_001","Jumping",1446047237252,"-0.995729","3.37279","-1.64837"
"user_001","Jumping",1446047237317,"-1.64717","-0.650847","1.41725"
"user_001","Jumping",1446047237381,"1.30349","-0.152682","1.45557"
"user_001","Jumping",1446047237446,"2.79798","0.000599","-0.460442"
"user_001","Jumping",1446047237510,"1.41845","-0.459245","-1.38013"
"user_001","Jumping",1446047237575,"3.90927","-6.62882","-1.49509"
"user_001","Jumping",1446047237640,"4.98224","-19.27452","1.57053"
"user_001","Jumping",1446047237705,"12.22478","-19.58108","2.0687"
"user_001","Jumping",1446047237769,"4.5224","-16.74538","-0.383802"
"user_001","Jumping",1446047237834,"0.690364","-0.880768","-0.537083"
"user_001","Jumping",1446047237899,"1.26517","2.18486","-1.03525"
"user_001","Jumping",1446047237964,"2.6447","-0.037722","1.18733"
"user_001","Jumping",1446047238028,"2.56806","-0.114362","1.03405"
"user_001","Jumping",1446047238094,"-1.14901","0.11556","-0.307161"
"user_001","Jumping",1446047238157,"-0.574206","-0.114362","-0.920286"
"user_001","Jumping",1446047238223,"2.60638","-3.40991","-0.422122"
"user_001","Jumping",1446047238287,"6.32345","-17.62674","-0.728685"
"user_001","Jumping",1446047238352,"10.53868","-19.58108","0.919089"
"user_001","Jumping",1446047238417,"7.85626","-18.20155","0.995729"
"user_001","Jumping",1446047238481,"-0.152682","-1.41725","-1.22685"
"user_001","Jumping",1446047238546,"1.26517","2.22318","-1.87829"
"user_001","Jumping",1446047238611,"0.537083","-0.497565","1.76214"
"user_001","Jumping",1446047238676,"1.95493","0.230521","1.60885"
"user_001","Jumping",1446047238741,"0.920286","0.537083","-0.422122"
"user_001","Jumping",1446047238806,"0.000599","-0.650847","0.497565"
"user_001","Jumping",1446047238870,"1.64837","-1.99206","-1.72501"
"user_001","Jumping",1446047238935,"5.0972","-14.75272","-1.41845"
"user_001","Jumping",1446047239000,"10.50036","-19.58108","1.91542"
"user_001","Jumping",1446047239064,"8.16283","-19.58108","1.95374"
"user_001","Jumping",1446047239129,"3.56439","-9.4262","-1.22685"
"user_001","Jumping",1446047239194,"0.920286","2.79798","-1.34181"
"user_001","Jumping",1446047239259,"0.1922","0.652044","-0.537083"
"user_001","Jumping",1446047239324,"1.64837","-0.957409","1.41725"
"user_001","Jumping",1446047239388,"1.99325","-0.191003","0.957409"
"user_001","Jumping",1446047239453,"1.26517","-0.305964","-0.000599"
"user_001","Jumping",1446047239518,"2.4531","-4.17632","-1.34181"
"user_001","Jumping",1446047239583,"4.44576","-16.05561","-0.537083"
"user_001","Jumping",1446047239647,"9.58068","-19.58108","1.53221"
"user_001","Jumping",1446047239712,"8.04786","-19.2362","2.0687"
"user_001","Jumping",1446047239777,"1.80165","-6.01569","-0.422122"
"user_001","Jumping",1446047239841,"0.11556","2.10822","-2.6447"
"user_001","Jumping",1446047239906,"0.345482","-0.612526","-1.22685"
"user_001","Jumping",1446047239971,"1.45677","-0.880768","2.2603"
"user_001","Jumping",1446047240036,"0.843646","1.11189","-0.537083"
"user_001","Jumping",1446047240100,"0.996927","0.038919","-0.575403"
"user_001","Jumping",1446047240166,"3.29615","-3.98471","-0.422122"
"user_001","Jumping",1446047240231,"5.21216","-16.7837","-0.613724"
"user_001","Jumping",1446047240295,"7.39642","-19.58108","1.11069"
"user_001","Jumping",1446047240360,"10.23212","-19.58108","0.459245"
"user_001","Jumping",1446047240424,"1.80165","-12.14694","-1.22685"
"user_001","Jumping",1446047240489,"0.575403","1.83997","0.037722"
"user_001","Jumping",1446047240553,"-0.152682","2.22318","-3.0279"
"user_001","Jumping",1446047240618,"0.268841","-1.91542","3.06503"
"user_001","Jumping",1446047240683,"1.72501","-0.535886","1.83878"
"user_001","Jumping",1446047240748,"-1.18733","0.996927","-1.26517"
"user_001","Jumping",1446047240813,"0.920286","-0.535886","-1.91661"
"user_001","Jumping",1446047240878,"4.40743","-11.61046","0.459245"
"user_001","Jumping",1446047240941,"6.78329","-19.58108","4.82776"
"user_001","Jumping",1446047241007,"9.35075","-19.58108","5.74745"
"user_001","Jumping",1446047241072,"5.71033","-15.74905","-1.15021"
"user_001","Jumping",1446047241136,"-1.57053","-1.80046","-0.15388"
"user_001","Jumping",1446047241201,"-0.267643","2.18486","-2.14654"
"user_001","Jumping",1446047241266,"2.22318","-1.80046","1.80046"
"user_001","Jumping",1446047241330,"-1.34061","0.767005","-2.03158"
"user_001","Jumping",1446047241395,"-0.382604","-0.842448","0.382604"
"user_001","Jumping",1446047241459,"-0.765807","-1.41725","0.076042"
"user_001","Jumping",1446047241525,"5.97857","-10.34589","-1.49509"
"user_001","Jumping",1446047241589,"11.34341","-19.58108","6.13065"
"user_001","Jumping",1446047241654,"10.15548","-19.58108","8.58315"
"user_001","Jumping",1446047241718,"4.67568","-11.61046","-0.307161"
"user_001","Jumping",1446047241783,"-1.37893","-0.650847","0.459245"
"user_001","Jumping",1446047241849,"-0.919089","2.6447","-3.14286"
"user_001","Jumping",1446047241913,"1.07357","-1.03405","-0.613724"
"user_001","Jumping",1446047241977,"0.15388","-0.919089","2.18366"
"user_001","Jumping",1446047242042,"-0.995729","0.460442","0.305964"
"user_001","Jumping",1446047242107,"2.03158","-0.382604","0.076042"
"user_001","Jumping",1446047242172,"6.93658","-10.84405","-0.422122"
"user_001","Jumping",1446047242237,"13.79591","-19.58108","1.49389"
"user_001","Jumping",1446047242302,"15.86521","-19.50444","-1.72501"
"user_001","Jumping",1446047242366,"4.79064","-12.03198","1.8771"
"user_001","Jumping",1446047242431,"-0.497565","1.45677","-4.10087"
"user_001","Jumping",1446047242495,"0.11556","0.613724","-1.30349"
"user_001","Jumping",1446047242560,"-0.459245","-0.305964","2.56686"
"user_001","Jumping",1446047242625,"2.29982","1.18853","-0.767005"
"user_001","Jumping",1446047242690,"1.38013","-0.305964","-1.87829"
"user_001","Jumping",1446047242754,"2.52974","-2.75846","0.152682"
"user_001","Jumping",1446047242819,"5.32712","-13.21991","-1.34181"
"user_001","Jumping",1446047242884,"10.50036","-19.58108","1.8771"
"user_001","Jumping",1446047242948,"13.02951","-19.58108","-0.498763"
"user_001","Jumping",1446047243013,"4.33079","-10.34589","-2.4531"
"user_001","Jumping",1446047243078,"0.613724","0.498763","2.49022"
"user_001","Jumping",1446047243143,"0.307161","1.53341","-2.49142"
"user_001","Jumping",1446047243207,"0.077239","-0.344284","1.91542"
"user_001","Jumping",1446047243272,"2.03158","0.1922","-0.000599"
"user_001","Jumping",1446047243337,"1.22685","0.230521","-1.87829"
"user_001","Jumping",1446047243401,"0.537083","-1.26397","0.535886"
"user_001","Jumping",1446047243467,"5.2888","-11.22725","0.689167"
"user_001","Jumping",1446047243531,"11.57333","-19.58108","0.344284"
"user_001","Jumping",1446047243596,"14.524","-19.58108","2.33694"
"user_001","Jumping",1446047243661,"3.18118","-11.8787","0.191003"
"user_001","Jumping",1446047243725,"-1.07237","1.15021","0.152682"
"user_001","Jumping",1446047243790,"0.422122","1.95493","-1.87829"
"user_001","Jumping",1446047243855,"-0.535886","-0.957409","1.18733"
"user_001","Jumping",1446047243920,"3.60271","-0.114362","0.574206"
"user_001","Jumping",1446047243984,"2.87462","-0.535886","-0.268841"
"user_001","Jumping",1446047244049,"2.2615","-1.53221","-0.11556"
"user_001","Jumping",1446047244114,"6.85994","-10.61413","-1.11189"
"user_001","Jumping",1446047244178,"7.97122","-19.58108","-0.077239"
"user_001","Jumping",1446047244243,"14.86888","-19.58108","-1.41845"
"user_001","Jumping",1446047244308,"8.50771","-14.48448","-0.613724"
"user_001","Jumping",1446047244373,"-0.535886","-1.18733","1.64717"
"user_001","Jumping",1446047244437,"-4.5212","3.37279","0.191003"
"user_001","Jumping",1446047244502,"-1.72382","0.690364","0.191003"
"user_003","Jumping",1446047778034,"-2.75846","-7.28026","-1.45677"
"user_003","Jumping",1446047778099,"-3.75479","-7.08866","-2.37646"
"user_003","Jumping",1446047778164,"-2.14534","-6.74378","-2.22318"
"user_003","Jumping",1446047778229,"-2.22198","-6.01569","-2.2615"
"user_003","Jumping",1446047778293,"-3.63983","-9.04299","-2.6447"
"user_003","Jumping",1446047778358,"-4.82776","-17.09026","-4.02423"
"user_003","Jumping",1446047778422,"-5.82409","-19.46612","-6.70665"
"user_003","Jumping",1446047778488,"0.805325","-10.92069","-1.49509"
"user_003","Jumping",1446047778552,"-4.67448","1.22685","1.64717"
"user_003","Jumping",1446047778616,"-1.37893","0.460442","-2.18486"
"user_003","Jumping",1446047778682,"0.1922","-0.650847","-0.077239"
"user_003","Jumping",1446047778746,"-0.842448","0.11556","-1.26517"
"user_003","Jumping",1446047778811,"-1.26397","-11.91702","-3.64103"
"user_003","Jumping",1446047778876,"-1.41725","-19.58108","-0.767005"
"user_003","Jumping",1446047778940,"-1.76214","-15.44249","-2.68302"
"user_003","Jumping",1446047779006,"-0.574206","-9.00468","-1.76333"
"user_003","Jumping",1446047779071,"-3.14167","-4.36792","-1.68669"
"user_003","Jumping",1446047779135,"-3.37159","-4.3296","-2.4531"
"user_003","Jumping",1446047779200,"-4.06135","-4.75112","-3.10454"
"user_003","Jumping",1446047779265,"-4.36792","-11.26557","-0.345482"
"user_003","Jumping",1446047779329,"-5.97737","-19.12124","-4.25415"
"user_003","Jumping",1446047779394,"-3.75479","-19.19788","-4.9056"
"user_003","Jumping",1446047779459,"-0.459245","-7.39522","-0.537083"
"user_003","Jumping",1446047779524,"-2.91174","3.10454","-2.37646"
"user_003","Jumping",1446047779589,"-1.41725","-0.995729","-0.230521"
"user_003","Jumping",1446047779653,"0.422122","-0.114362","0.344284"
"user_003","Jumping",1446047779718,"-0.574206","-0.535886","-1.18853"
"user_003","Jumping",1446047779783,"-1.72382","-13.98632","-4.10087"
"user_003","Jumping",1446047779847,"-3.14167","-19.58108","-3.48775"
"user_003","Jumping",1446047779912,"-2.68182","-13.21991","-3.18118"
"user_003","Jumping",1446047779977,"-0.267643","-8.92803","-2.10822"
"user_003","Jumping",1446047780042,"-3.17999","-3.98471","-0.728685"
"user_003","Jumping",1446047780107,"-2.91174","-4.48288","-2.18486"
"user_003","Jumping",1446047780171,"-3.37159","-5.78577","-3.87095"
"user_003","Jumping",1446047780236,"-4.02303","-9.84772","-0.383802"
"user_003","Jumping",1446047780301,"-6.47553","-17.81835","-4.13919"
"user_003","Jumping",1446047780365,"-3.86975","-19.58108","-5.40376"
"user_003","Jumping",1446047780430,"-1.99206","-14.44616","-3.64103"
"user_003","Jumping",1446047780495,"-2.29862","-1.18733","-0.307161"
"user_003","Jumping",1446047780559,"-1.8771","1.99325","-1.68669"
"user_003","Jumping",1446047780625,"0.000599","-0.344284","0.650847"
"user_003","Jumping",1446047780689,"-0.459245","-0.191003","-0.613724"
"user_003","Jumping",1446047780754,"-2.60518","-6.9737","-4.714"
"user_003","Jumping",1446047780818,"-1.41725","-19.58108","-2.56806"
"user_003","Jumping",1446047780884,"-3.40991","-17.51178","-4.59904"
"user_003","Jumping",1446047780948,"-1.95374","-10.61413","-2.91294"
"user_003","Jumping",1446047781013,"-3.06503","-6.70546","-1.76333"
"user_003","Jumping",1446047781078,"-2.6435","-7.20362","-1.87829"
"user_003","Jumping",1446047781143,"-2.60518","-7.28026","-1.64837"
"user_003","Jumping",1446047781208,"-1.72382","-6.51385","-2.0699"
"user_003","Jumping",1446047781272,"-0.689167","-5.55585","-2.37646"
"user_003","Jumping",1446047781337,"-3.67815","-8.00835","-1.99325"
"user_003","Jumping",1446047781402,"-5.63249","-13.64143","-3.44943"
"user_003","Jumping",1446047781467,"-5.97737","-18.16323","-5.59536"
"user_003","Jumping",1446047781531,"-2.03038","-19.2362","-5.25048"
"user_003","Jumping",1446047781596,"-2.14534","-8.88971","-1.18853"
"user_003","Jumping",1446047781660,"-3.06503","2.52974","-0.767005"
"user_003","Jumping",1446047781725,"0.230521","0.268841","-0.345482"
"user_003","Jumping",1446047781790,"0.15388","-0.535886","-0.690364"
"user_003","Jumping",1446047781855,"-0.420925","-2.18366","-2.68302"
"user_003","Jumping",1446047781919,"-3.63983","-17.47346","-4.44576"
"user_003","Jumping",1446047781985,"-2.91174","-18.35483","-4.714"
"user_003","Jumping",1446047782049,"-0.919089","-10.88237","-2.2615"
"user_003","Jumping",1446047782114,"-3.02671","-6.93538","-2.68302"
"user_003","Jumping",1446047782178,"-2.75846","-7.3569","-2.10822"
"user_003","Jumping",1446047782244,"-1.45557","-7.85507","-1.22685"
"user_003","Jumping",1446047782309,"-1.8771","-6.32225","-1.99325"
"user_003","Jumping",1446047782373,"-1.64717","-5.90073","-2.33814"
"user_003","Jumping",1446047782438,"-2.49022","-8.08499","-1.80165"
"user_003","Jumping",1446047782503,"-4.40624","-11.26557","-2.37646"
"user_003","Jumping",1446047782568,"-5.13432","-17.1669","-4.67568"
"user_003","Jumping",1446047782632,"-3.40991","-19.54276","-5.71033"
"user_003","Jumping",1446047782697,"-1.99206","-13.48815","-4.06255"
"user_003","Jumping",1446047782762,"-2.22198","-1.68549","0.267643"
"user_003","Jumping",1446047782827,"-1.91542","1.80165","-1.68669"
"user_003","Jumping",1446047782891,"0.575403","-0.152682","0.459245"
"user_003","Jumping",1446047782956,"-0.842448","-0.804128","-1.03525"
"user_003","Jumping",1446047783021,"-1.76214","-12.22358","-5.67201"
"user_003","Jumping",1446047783086,"-2.33694","-19.19788","-4.02423"
"user_003","Jumping",1446047783151,"-1.14901","-14.98264","-4.13919"
"user_003","Jumping",1446047783215,"-2.22198","-9.04299","-2.91294"
"user_003","Jumping",1446047783280,"-2.41358","-5.59417","-0.613724"
"user_003","Jumping",1446047783344,"-2.60518","-5.78577","-2.33814"
"user_003","Jumping",1446047783409,"-3.67815","-4.44456","-2.4531"
"user_003","Jumping",1446047783474,"-2.8351","-4.63616","-3.75599"
"user_003","Jumping",1446047783539,"-1.64717","-16.4005","-0.843646"
"user_003","Jumping",1446047783604,"-6.62882","-19.58108","-8.58435"
"user_003","Jumping",1446047783669,"-1.72382","-14.67608","-3.52607"
"user_003","Jumping",1446047783733,"-1.57053","1.34181","0.612526"
"user_003","Jumping",1446047783798,"-1.18733","1.22685","-1.38013"
"user_003","Jumping",1446047783863,"-0.612526","-0.612526","-0.268841"
"user_003","Jumping",1446047783928,"-1.22565","0.345482","-0.805325"
"user_003","Jumping",1446047783992,"-1.11069","-2.2603","-2.56806"
"user_003","Jumping",1446047784057,"-2.8351","-17.62674","-5.02056"
"user_003","Jumping",1446047784122,"-3.90807","-19.19788","-5.59536"
"user_003","Jumping",1446047784187,"-1.72382","-12.6451","-3.64103"
"user_003","Jumping",1446047784252,"-1.49389","-6.7821","-2.0699"
"user_003","Jumping",1446047784316,"-2.14534","-4.67448","-1.15021"
"user_003","Jumping",1446047784381,"-2.91174","-6.93538","-2.6447"
"user_003","Jumping",1446047784446,"-3.71647","-5.86241","-2.56806"
"user_003","Jumping",1446047784510,"-0.344284","-6.51385","-2.8363"
"user_003","Jumping",1446047784575,"-4.02303","-14.98264","-2.2615"
"user_003","Jumping",1446047784639,"-5.36425","-19.58108","-8.89091"
"user_003","Jumping",1446047784704,"-0.191003","-14.906","-3.94759"
"user_003","Jumping",1446047784769,"-2.4519","0.077239","1.11069"
"user_003","Jumping",1446047784834,"-2.4519","1.22685","-1.41845"
"user_003","Jumping",1446047784899,"-0.344284","0.345482","-0.15388"
"user_003","Jumping",1446047784964,"-0.842448","0.422122","-0.996927"
"user_003","Jumping",1446047785029,"-2.29862","-4.3296","-4.63736"
"user_003","Jumping",1446047785093,"-0.765807","-18.92964","-3.98591"
"user_003","Jumping",1446047785158,"-4.3296","-19.38948","-5.17384"
"user_003","Jumping",1446047785222,"-1.11069","-12.6451","-1.22685"
"user_003","Jumping",1446047785287,"-3.44823","-7.24194","-2.18486"
"user_003","Jumping",1446047785352,"-2.49022","-6.51385","-2.95126"
"user_003","Jumping",1446047785417,"-2.10702","-6.85874","-1.22685"
"user_003","Jumping",1446047785482,"-2.79678","-7.05034","-2.14654"
"user_003","Jumping",1446047785546,"-2.56686","-5.01936","-2.52974"
"user_003","Jumping",1446047785611,"-2.87342","-6.62882","-2.56806"
"user_003","Jumping",1446047785676,"-4.63616","-14.44616","-2.52974"
"user_003","Jumping",1446047785741,"-5.67081","-19.58108","-6.59169"
"user_003","Jumping",1446047785805,"-3.67815","-17.66507","-6.63001"
"user_003","Jumping",1446047785870,"-0.919089","-4.82776","-0.268841"
"user_003","Jumping",1446047785934,"-2.91174","2.87462","-2.33814"
"user_003","Jumping",1446047785999,"-0.420925","-0.420925","0.919089"
"user_003","Jumping",1446047786065,"-0.037722","0.230521","0.382604"
"user_003","Jumping",1446047786129,"-0.842448","-0.497565","-3.0279"
"user_003","Jumping",1446047786194,"-0.995729","-15.48081","-4.33079"
"user_003","Jumping",1446047786259,"-3.98471","-19.50444","-6.89825"
"user_003","Jumping",1446047786324,"-0.574206","-13.29655","-3.94759"
"user_003","Jumping",1446047786388,"-1.41725","-8.85139","-1.38013"
"user_003","Jumping",1446047786453,"-3.94639","-5.59417","-3.14286"
"user_003","Jumping",1446047786518,"-2.18366","-8.04667","-3.06622"
"user_003","Jumping",1446047786582,"-1.60885","-8.92803","-1.15021"
"user_003","Jumping",1446047786647,"-2.72014","-5.13432","-2.33814"
"user_003","Jumping",1446047786712,"-2.03038","-4.25296","-3.18118"
"user_003","Jumping",1446047786777,"-3.48655","-9.84772","-1.45677"
"user_003","Jumping",1446047786841,"-6.01569","-18.96796","-6.78329"
"user_003","Jumping",1446047786906,"-4.44456","-19.4278","-8.62267"
"user_003","Jumping",1446047786970,"-0.267643","-8.92803","-1.57173"
"user_003","Jumping",1446047787035,"-2.91174","3.41111","-1.53341"
"user_003","Jumping",1446047787100,"0.268841","-0.765807","0.689167"
"user_003","Jumping",1446047787165,"-0.650847","0.383802","-0.728685"
"user_003","Jumping",1446047787230,"-0.880768","0.422122","-1.03525"
"user_003","Jumping",1446047787294,"-1.60885","-11.11229","-5.4804"
"user_003","Jumping",1446047787358,"-4.59784","-19.35116","-5.94025"
"user_003","Jumping",1446047787424,"-2.41358","-15.71073","-5.74865"
"user_003","Jumping",1446047787489,"-1.26397","-9.34956","-3.06622"
"user_003","Jumping",1446047787553,"-3.44823","-6.47553","-2.75966"
"user_003","Jumping",1446047787618,"-2.4519","-7.97003","-2.2615"
"user_003","Jumping",1446047787683,"-1.95374","-7.81675","-1.83997"
"user_003","Jumping",1446047787747,"-2.75846","-4.67448","-3.06622"
"user_003","Jumping",1446047787812,"-2.22198","-5.24928","-2.0699"
"user_003","Jumping",1446047787877,"-3.29495","-12.41518","-1.49509"
"user_003","Jumping",1446047787942,"-5.44089","-19.46612","-6.20849"
"user_003","Jumping",1446047788007,"-3.79311","-18.69972","-7.70298"
"user_003","Jumping",1446047788071,"-0.382604","-7.1653","-1.83997"
"user_003","Jumping",1446047788136,"-3.14167","2.72134","-0.575403"
"user_003","Jumping",1446047788201,"0.460442","-0.152682","0.114362"
"user_003","Jumping",1446047788265,"-0.842448","0.652044","-0.230521"
"user_003","Jumping",1446047788331,"-2.2603","-0.229323","-1.45677"
"user_003","Jumping",1446047788395,"-1.45557","-13.71807","-6.63001"
"user_003","Jumping",1446047788460,"-3.14167","-19.54276","-6.32345"
"user_003","Jumping",1446047788524,"-1.83878","-13.06663","-4.40743"
"user_003","Jumping",1446047788590,"-1.83878","-9.77108","-3.25783"
"user_003","Jumping",1446047788654,"-3.14167","-6.55217","-2.6447"
"user_003","Jumping",1446047788719,"-2.10702","-7.31858","-2.10822"
"user_003","Jumping",1446047788784,"-2.14534","-5.90073","-2.0699"
"user_003","Jumping",1446047788848,"-1.57053","-4.9044","-2.75966"
"user_003","Jumping",1446047788913,"-2.75846","-7.5485","-1.76333"
"user_003","Jumping",1446047788978,"-4.98104","-15.97897","-4.67568"
"user_003","Jumping",1446047789043,"-3.10335","-19.58108","-8.16283"
"user_003","Jumping",1446047789107,"-1.68549","-16.36218","-5.25048"
"user_003","Jumping",1446047789172,"-2.52854","-2.0687","-0.345482"
"user_003","Jumping",1446047789236,"-1.72382","2.41478","-2.37646"
"user_003","Jumping",1446047789301,"0.843646","-0.382604","1.11069"
"user_003","Jumping",1446047789366,"-0.689167","0.230521","-0.728685"
"user_003","Jumping",1446047789431,"-1.14901","-1.95374","-2.72134"
"user_003","Jumping",1446047789495,"-3.90807","-17.74171","-7.58802"
"user_003","Jumping",1446047789560,"-3.29495","-18.96796","-5.05888"
"user_003","Jumping",1446047789625,"-2.22198","-12.0703","-3.98591"
"user_003","Jumping",1446047789690,"-2.79678","-7.7401","-2.37646"
"user_003","Jumping",1446047789755,"-1.95374","-5.74745","-2.22318"
"user_003","Jumping",1446047789818,"-2.03038","-6.85874","-1.68669"
"user_003","Jumping",1446047789883,"-2.91174","-5.67081","-2.52974"
"user_003","Jumping",1446047789949,"-1.49389","-4.94272","-2.75966"
"user_003","Jumping",1446047790013,"-3.37159","-10.88237","-1.57173"
"user_003","Jumping",1446047790078,"-5.51753","-19.58108","-7.24314"
"user_003","Jumping",1446047790143,"-4.48288","-17.5501","-6.9749"
"user_003","Jumping",1446047790207,"-0.612526","-2.68182","-0.613724"
"user_003","Jumping",1446047790272,"-1.8771","2.68302","-2.03158"
"user_003","Jumping",1446047790337,"1.15021","-0.919089","1.64717"
"user_003","Jumping",1446047790402,"-2.03038","0.307161","-1.22685"
"user_003","Jumping",1446047790467,"-1.34061","-0.191003","-1.61005"
"user_003","Jumping",1446047790531,"-3.02671","-13.64143","-5.17384"
"user_003","Jumping",1446047790596,"-4.75112","-19.58108","-7.62634"
"user_003","Jumping",1446047790660,"-2.60518","-15.05928","-6.01689"
"user_003","Jumping",1446047790726,"-1.26397","-8.12331","-3.10454"
"user_003","Jumping",1446047790790,"-3.25663","-5.97737","-3.0279"
"user_003","Jumping",1446047790855,"-0.420925","-6.24561","-2.2615"
"user_003","Jumping",1446047790919,"-1.83878","-3.40991","-3.60271"
"user_003","Jumping",1446047790984,"-3.94639","-5.36425","-3.75599"
"user_003","Jumping",1446047791049,"-2.2603","-13.98632","-3.67935"
"user_003","Jumping",1446047791114,"-7.70178","-19.58108","-7.5497"
"user_003","Jumping",1446047791179,"-2.52854","-15.36585","-6.66833"
"user_003","Jumping",1446047791243,"-0.574206","-0.497565","-0.230521"
"user_003","Jumping",1446047791308,"-2.0687","0.958607","-0.575403"
"user_003","Jumping",1446047791373,"0.11556","-0.497565","0.344284"
"user_003","Jumping",1446047791438,"-1.30229","1.34181","-1.11189"
"user_003","Jumping",1446047791503,"-0.382604","-0.880768","-2.22318"
"user_003","Jumping",1446047791568,"-2.87342","-15.97897","-6.28513"
"user_003","Jumping",1446047791632,"-2.95007","-19.38948","-7.51138"
"user_003","Jumping",1446047791697,"-4.59784","-13.37319","-5.02056"
"user_003","Jumping",1446047791762,"-2.14534","-9.84772","-3.2195"
"user_003","Jumping",1446047791827,"-2.29862","-6.55217","-2.49142"
"user_003","Jumping",1446047791891,"-1.68549","-7.47186","-2.29982"
"user_003","Jumping",1446047791956,"-1.22565","-8.16163","-2.56806"
"user_003","Jumping",1446047792021,"-2.95007","-5.096","-2.52974"
"user_003","Jumping",1446047792085,"-4.02303","-4.06135","-7.7413"
"user_003","Jumping",1446047792151,"-2.60518","-14.40784","0.727487"
"user_003","Jumping",1446047792215,"-6.24561","-19.58108","-8.89091"
"user_003","Jumping",1446047792280,"-1.60885","-12.56846","-3.2195"
"user_003","Jumping",1446047792344,"-3.10335","3.14286","-1.26517"
"user_003","Jumping",1446047792410,"-0.037722","0.000599","-0.11556"
"user_003","Jumping",1446047792474,"0.268841","-0.650847","0.114362"
"user_003","Jumping",1446047792539,"-2.68182","0.996927","-1.99325"
"user_003","Jumping",1446047792603,"-1.64717","-4.67448","-5.51872"
"user_003","Jumping",1446047792669,"-2.87342","-18.96796","-5.86361"
"user_003","Jumping",1446047792733,"-3.29495","-18.66139","-4.44576"
"user_003","Jumping",1446047792798,"-4.02303","-13.21991","-4.79064"
"user_003","Jumping",1446047792863,"-2.95007","-7.97003","-2.79798"
"user_003","Jumping",1446047792928,"-3.17999","-6.20729","-2.29982"
"user_003","Jumping",1446047792992,"-3.25663","-5.40257","-2.37646"
"user_003","Jumping",1446047793057,"-3.40991","-4.5212","-3.18118"
"user_003","Jumping",1446047793122,"-3.29495","-3.90807","-3.94759"
"user_003","Jumping",1446047793186,"-4.7128","-14.7144","-2.75966"
"user_003","Jumping",1446047793251,"-4.55952","-19.58108","-7.77962"
"user_003","Jumping",1446047793316,"-2.95007","-16.32385","-6.93658"
"user_003","Jumping",1446047793380,"-1.26397","-2.14534","-1.38013"
"user_003","Jumping",1446047793445,"-2.10702","2.33814","-1.64837"
"user_003","Jumping",1446047793510,"-0.420925","-0.267643","1.07237"
"user_003","Jumping",1446047793575,"-0.995729","0.383802","-0.537083"
"user_003","Jumping",1446047793640,"-1.14901","-1.76214","-2.29982"
"user_003","Jumping",1446047793704,"-2.56686","-14.82936","-6.28513"
"user_003","Jumping",1446047793768,"-3.40991","-19.31284","-7.3581"
"user_003","Jumping",1446047793834,"-3.52487","-13.87135","-5.63368"
"user_003","Jumping",1446047793899,"-2.68182","-10.84405","-3.10454"
"user_003","Jumping",1446047793963,"-2.72014","-4.78944","-2.72134"
"user_003","Jumping",1446047794028,"-1.03405","-4.94272","-1.68669"
"user_003","Jumping",1446047794093,"-2.10702","-5.93905","-2.14654"
"user_003","Jumping",1446047794158,"-1.57053","-5.90073","-3.2195"
"user_003","Jumping",1446047794222,"-4.3296","-8.08499","-1.15021"
"user_003","Jumping",1446047794287,"-4.48288","-19.08292","-6.36177"
"user_003","Jumping",1446047794352,"-4.67448","-19.58108","-8.27779"
"user_003","Jumping",1446047794416,"0.000599","-10.76741","-2.33814"
"user_003","Jumping",1446047794481,"-1.95374","3.41111","-1.07357"
"user_003","Jumping",1446047794546,"-0.152682","-0.689167","0.229323"
"user_003","Jumping",1446047794611,"-0.957409","0.422122","-0.460442"
"user_003","Jumping",1446047794676,"-0.957409","0.996927","-0.881966"
"user_003","Jumping",1446047794740,"-1.57053","-3.60151","-4.75232"
"user_003","Jumping",1446047794805,"-1.99206","-18.35483","-5.78697"
"user_003","Jumping",1446047794870,"-3.71647","-19.2362","-6.47673"
"user_003","Jumping",1446047794935,"-0.842448","-14.94432","-4.17751"
"user_002","Standing",1446309439342,"-2.49022","-9.19628","-1.11189"
"user_002","Standing",1446309439349,"-2.41358","-9.15796","-1.22685"
"user_002","Standing",1446309439358,"-2.56686","-9.15796","-1.30349"
"user_002","Standing",1446309439365,"-2.75846","-9.08132","-1.11189"
"user_002","Standing",1446309439373,"-2.75846","-9.08132","-1.15021"
"user_002","Standing",1446309439382,"-2.8351","-9.15796","-0.920286"
"user_002","Standing",1446309439390,"-2.8357","-9.15855","-0.919687"
"user_002","Standing",1446309439398,"-2.95007","-9.2346","-0.881966"
"user_002","Standing",1446309439406,"-2.8351","-9.11964","-0.958607"
"user_002","Standing",1446309439414,"-2.8351","-8.96635","-1.15021"
"user_002","Standing",1446309439422,"-2.95007","-9.08132","-0.920286"
"user_002","Standing",1446309439430,"-2.95007","-8.88971","-0.996927"
"user_002","Standing",1446309439438,"-2.91174","-9.00468","-1.15021"
"user_002","Standing",1446309439447,"-3.02671","-8.96635","-0.996927"
"user_002","Standing",1446309439454,"-2.79678","-8.96635","-1.15021"
"user_002","Standing",1446309439463,"-2.87342","-9.08132","-1.34181"
"user_002","Standing",1446309439471,"-2.98839","-9.19628","-1.22685"
"user_002","Standing",1446309439479,"-2.95007","-9.00468","-1.30349"
"user_002","Standing",1446309439487,"-2.98839","-9.00468","-1.15021"
"user_002","Standing",1446309439495,"-3.06503","-9.00468","-0.958607"
"user_002","Standing",1446309439503,"-3.02671","-9.04299","-0.881966"
"user_002","Standing",1446309439511,"-2.91174","-9.00468","-0.920286"
"user_002","Standing",1446309439519,"-2.95007","-9.08132","-0.767005"
"user_002","Standing",1446309439527,"-2.8351","-9.00468","-0.958607"
"user_002","Standing",1446309439536,"-2.95007","-9.00468","-0.996927"
"user_002","Standing",1446309439543,"-2.75846","-9.04299","-0.690364"
"user_002","Standing",1446309439552,"-2.6435","-9.00468","-1.03525"
"user_002","Standing",1446309439560,"-2.72014","-9.00468","-1.15021"
"user_002","Standing",1446309439568,"-2.68182","-9.04299","-1.11189"
"user_002","Standing",1446309439576,"-2.60518","-9.00468","-1.11189"
"user_002","Standing",1446309439584,"-2.52854","-8.88971","-1.15021"
"user_002","Standing",1446309439593,"-2.6435","-8.92803","-1.03525"
"user_002","Standing",1446309439601,"-2.60518","-8.96635","-0.996927"
"user_002","Standing",1446309439609,"-2.6435","-9.04299","-0.996927"
"user_002","Standing",1446309439616,"-2.75846","-9.04299","-0.920286"
"user_002","Standing",1446309439624,"-2.6435","-9.19628","-0.805325"
"user_002","Standing",1446309439633,"-2.79678","-8.96635","-0.767005"
"user_002","Standing",1446309439641,"-2.87342","-8.92803","-0.958607"
"user_002","Standing",1446309439649,"-2.60518","-9.11964","-0.843646"
"user_002","Standing",1446309439657,"-2.8351","-9.08132","-0.843646"
"user_002","Standing",1446309439665,"-2.8351","-9.2346","-0.920286"
"user_002","Standing",1446309439674,"-2.72014","-9.11964","-0.958607"
"user_002","Standing",1446309439682,"-2.87342","-9.15796","-1.18853"
"user_002","Standing",1446309439690,"-3.06503","-9.2346","-1.18853"
"user_002","Standing",1446309439698,"-2.8351","-9.08132","-0.996927"
"user_002","Standing",1446309439705,"-2.98839","-9.04299","-1.03525"
"user_002","Standing",1446309439713,"-3.17999","-8.96635","-1.18853"
"user_002","Standing",1446309439722,"-3.21831","-8.88971","-1.18853"
"user_002","Standing",1446309439729,"-3.17999","-8.92803","-1.15021"
"user_002","Standing",1446309439738,"-3.17999","-8.77475","-1.22685"
"user_002","Standing",1446309439746,"-3.33327","-8.65979","-1.07357"
"user_002","Standing",1446309439754,"-3.37159","-8.58315","-1.34181"
"user_002","Standing",1446309439762,"-3.33327","-8.77475","-1.18853"
"user_002","Standing",1446309439771,"-3.33327","-8.58315","-1.07357"
"user_002","Standing",1446309439779,"-3.14167","-8.92803","-1.03525"
"user_002","Standing",1446309439787,"-3.25663","-8.85139","-0.881966"
"user_002","Standing",1446309439795,"-3.10335","-8.85139","-0.920286"
"user_002","Standing",1446309439803,"-2.87342","-8.85139","-0.843646"
"user_002","Standing",1446309439811,"-2.91174","-8.92803","-0.767005"
"user_002","Standing",1446309439819,"-2.87342","-8.88971","-0.805325"
"user_002","Standing",1446309439827,"-2.98839","-8.96635","-0.767005"
"user_002","Standing",1446309439835,"-2.79678","-9.11964","-1.07357"
"user_002","Standing",1446309439843,"-2.87342","-9.08132","-0.920286"
"user_002","Standing",1446309439851,"-2.95007","-9.15796","-0.805325"
"user_002","Standing",1446309439859,"-3.02671","-8.85139","-0.728685"
"user_002","Standing",1446309439868,"-2.98839","-8.92803","-0.690364"
"user_002","Standing",1446309439875,"-2.98839","-8.96635","-0.690364"
"user_002","Standing",1446309439883,"-2.91174","-8.96635","-0.881966"
"user_002","Standing",1446309439892,"-3.02671","-8.85139","-0.613724"
"user_002","Standing",1446309439900,"-3.06503","-9.04299","-0.767005"
"user_002","Standing",1446309439908,"-2.87342","-8.73643","-0.652044"
"user_002","Standing",1446309439916,"-2.95007","-8.88971","-0.728685"
"user_002","Standing",1446309439924,"-3.10335","-8.77475","-0.690364"
"user_002","Standing",1446309439932,"-2.91174","-8.77475","-0.652044"
"user_002","Standing",1446309439940,"-2.91174","-9.04299","-0.805325"
"user_002","Standing",1446309439948,"-2.91174","-9.04299","-0.767005"
"user_002","Standing",1446309439957,"-3.02671","-9.04299","-0.652044"
"user_002","Standing",1446309439965,"-3.06503","-9.27292","-0.805325"
"user_002","Standing",1446309439973,"-3.14167","-9.00468","-0.767005"
"user_002","Standing",1446309439980,"-2.95007","-8.96635","-0.843646"
"user_002","Standing",1446309439989,"-3.10335","-9.00468","-1.15021"
"user_002","Standing",1446309439997,"-2.98839","-9.19628","-1.03525"
"user_002","Standing",1446309440004,"-3.10335","-9.19628","-1.15021"
"user_002","Standing",1446309440013,"-2.91174","-9.11964","-1.18853"
"user_002","Standing",1446309440021,"-2.91174","-9.08132","-1.22685"
"user_002","Standing",1446309440029,"-2.95007","-9.00468","-1.26517"
"user_002","Standing",1446309440038,"-3.06503","-9.08132","-1.15021"
"user_002","Standing",1446309440045,"-3.06503","-9.00468","-1.03525"
"user_002","Standing",1446309440054,"-3.10335","-9.11964","-1.07357"
"user_002","Standing",1446309440062,"-3.10335","-8.96635","-0.996927"
"user_002","Standing",1446309440069,"-3.10335","-8.96635","-1.03525"
"user_002","Standing",1446309440078,"-3.17999","-9.00468","-1.11189"
"user_002","Standing",1446309440086,"-3.14167","-8.85139","-0.996927"
"user_002","Standing",1446309440094,"-3.17999","-9.00468","-0.728685"
"user_002","Standing",1446309440102,"-3.17999","-8.96635","-0.958607"
"user_002","Standing",1446309440111,"-3.10335","-9.00468","-0.881966"
"user_002","Standing",1446309440119,"-3.17999","-9.00468","-1.03525"
"user_002","Standing",1446309440126,"-3.02671","-9.00468","-0.881966"
"user_002","Standing",1446309440135,"-3.06503","-9.00468","-0.728685"
"user_002","Standing",1446309440142,"-3.21831","-8.92803","-1.03525"
"user_002","Standing",1446309440151,"-3.33327","-8.85139","-0.767005"
"user_002","Standing",1446309440158,"-3.21831","-9.04299","-0.652044"
"user_002","Standing",1446309440167,"-3.33327","-8.85139","-0.613724"
"user_002","Standing",1446309440175,"-3.37159","-8.85139","-0.345482"
"user_002","Standing",1446309440183,"-3.37159","-9.00468","-0.652044"
"user_002","Standing",1446309440192,"-3.33327","-8.81307","-0.460442"
"user_002","Standing",1446309440199,"-3.21831","-9.08132","-0.537083"
"user_002","Standing",1446309440208,"-3.21831","-8.92803","-0.690364"
"user_002","Standing",1446309440215,"-3.14167","-8.96635","-0.575403"
"user_002","Standing",1446309440224,"-3.17999","-8.92803","-0.690364"
"user_002","Standing",1446309440232,"-3.17999","-8.77475","-0.690364"
"user_002","Standing",1446309440239,"-3.14167","-8.88971","-0.805325"
"user_002","Standing",1446309440248,"-3.10335","-9.04299","-0.843646"
"user_002","Standing",1446309440255,"-3.14167","-8.88971","-0.767005"
"user_002","Standing",1446309440264,"-3.06503","-9.11964","-0.805325"
"user_002","Standing",1446309440272,"-3.14167","-8.96635","-0.690364"
"user_002","Standing",1446309440280,"-3.29495","-9.08132","-0.843646"
"user_002","Standing",1446309440288,"-3.33327","-8.81307","-0.575403"
"user_002","Standing",1446309440296,"-3.21831","-8.88971","-0.575403"
"user_002","Standing",1446309440305,"-3.21831","-8.96635","-0.613724"
"user_002","Standing",1446309440312,"-3.14167","-8.77475","-0.537083"
"user_002","Standing",1446309440320,"-3.10335","-8.85139","-0.613724"
"user_002","Standing",1446309440329,"-3.21831","-9.00468","-0.613724"
"user_002","Standing",1446309440337,"-3.14167","-8.85139","-0.613724"
"user_002","Standing",1446309440345,"-3.21831","-9.04299","-0.690364"
"user_002","Standing",1446309440353,"-3.06503","-8.92803","-0.575403"
"user_002","Standing",1446309440361,"-3.25663","-9.11964","-0.728685"
"user_002","Standing",1446309440369,"-3.21831","-8.92803","-0.652044"
"user_002","Standing",1446309440377,"-3.06503","-8.92803","-0.843646"
"user_002","Standing",1446309440385,"-3.06503","-9.04299","-0.690364"
"user_002","Standing",1446309440394,"-3.21831","-9.00468","-0.460442"
"user_002","Standing",1446309440402,"-3.02671","-8.92803","-0.652044"
"user_002","Standing",1446309440410,"-3.21831","-8.96635","-0.652044"
"user_002","Standing",1446309440417,"-3.25663","-8.96635","-0.767005"
"user_002","Standing",1446309440426,"-3.21831","-8.88971","-0.728685"
"user_002","Standing",1446309440434,"-3.37159","-8.85139","-0.767005"
"user_002","Standing",1446309440443,"-3.21831","-8.85139","-0.537083"
"user_002","Standing",1446309440450,"-3.10335","-9.11964","-0.767005"
"user_002","Standing",1446309440458,"-3.10335","-9.19628","-0.920286"
"user_002","Standing",1446309440467,"-3.10335","-9.00468","-0.767005"
"user_002","Standing",1446309440474,"-3.02671","-8.96635","-0.767005"
"user_002","Standing",1446309440482,"-3.10335","-8.92803","-0.805325"
"user_002","Standing",1446309440491,"-3.17999","-8.92803","-0.843646"
"user_002","Standing",1446309440498,"-3.33327","-8.96635","-0.805325"
"user_002","Standing",1446309440506,"-3.29495","-8.85139","-0.575403"
"user_002","Standing",1446309440515,"-3.21831","-8.88971","-0.728685"
"user_002","Standing",1446309440523,"-3.29495","-8.92803","-0.652044"
"user_002","Standing",1446309440531,"-3.37159","-9.04299","-0.575403"
"user_002","Standing",1446309440539,"-3.33327","-8.92803","-0.613724"
"user_002","Standing",1446309440547,"-3.33327","-8.88971","-0.498763"
"user_002","Standing",1446309440555,"-3.21831","-9.08132","-0.690364"
"user_002","Standing",1446309440564,"-3.06503","-8.88971","-0.652044"
"user_002","Standing",1446309440571,"-3.17999","-8.88971","-0.537083"
"user_002","Standing",1446309440580,"-3.06503","-8.88971","-0.498763"
"user_002","Standing",1446309440587,"-3.10335","-8.85139","-0.575403"
"user_002","Standing",1446309440596,"-3.06503","-9.08132","-0.613724"
"user_002","Standing",1446309440604,"-2.87342","-8.85139","-0.652044"
"user_002","Standing",1446309440612,"-2.95007","-9.08132","-0.728685"
"user_002","Standing",1446309440620,"-3.14167","-8.96635","-0.422122"
"user_002","Standing",1446309440628,"-3.02671","-8.92803","-0.537083"
"user_002","Standing",1446309440636,"-3.14167","-9.00468","-0.422122"
"user_002","Standing",1446309440645,"-3.10335","-9.08132","-0.383802"
"user_002","Standing",1446309440653,"-3.14167","-8.81307","-0.307161"
"user_002","Standing",1446309440660,"-3.17999","-8.85139","-0.422122"
"user_002","Standing",1446309440669,"-3.21831","-8.85139","-0.1922"
"user_002","Standing",1446309440676,"-3.17999","-9.04299","-0.498763"
"user_002","Standing",1446309440685,"-3.02671","-9.04299","-0.460442"
"user_002","Standing",1446309440693,"-3.21831","-8.85139","-0.498763"
"user_002","Standing",1446309440701,"-3.10335","-9.08132","-0.498763"
"user_002","Standing",1446309440709,"-3.29495","-8.96635","-0.537083"
"user_002","Standing",1446309440717,"-3.06503","-8.88971","-0.383802"
"user_002","Standing",1446309440725,"-3.10335","-9.15796","-0.498763"
"user_002","Standing",1446309440734,"-2.91174","-9.00468","-0.345482"
"user_002","Standing",1446309440741,"-3.17999","-8.88971","-0.498763"
"user_002","Standing",1446309440749,"-3.21831","-9.08132","-0.537083"
"user_002","Standing",1446309440757,"-3.06503","-9.00468","-0.460442"
"user_002","Standing",1446309440766,"-3.06503","-9.00468","-0.613724"
"user_002","Standing",1446309440774,"-3.10335","-8.92803","-0.498763"
"user_002","Standing",1446309440782,"-3.25663","-8.92803","-0.613724"
"user_002","Standing",1446309440790,"-3.17999","-8.92803","-0.728685"
"user_002","Standing",1446309440798,"-3.29495","-9.04299","-0.422122"
"user_002","Standing",1446309440806,"-3.17999","-9.08132","-0.422122"
"user_002","Standing",1446309440814,"-3.21831","-9.00468","-0.460442"
"user_002","Standing",1446309440822,"-3.06503","-8.96635","-0.460442"
"user_002","Standing",1446309440831,"-3.17999","-9.04299","-0.537083"
"user_002","Standing",1446309440839,"-3.14167","-9.04299","-0.498763"
"user_002","Standing",1446309440847,"-3.17999","-9.08132","-0.345482"
"user_002","Standing",1446309440855,"-3.21831","-9.08132","-0.613724"
"user_002","Standing",1446309440863,"-3.33327","-9.15796","-0.460442"
"user_002","Standing",1446309440871,"-3.37159","-9.00468","-0.460442"
"user_002","Standing",1446309440879,"-3.17999","-8.92803","-0.537083"
"user_002","Standing",1446309440887,"-3.17999","-8.96635","-0.345482"
"user_002","Standing",1446309440896,"-3.17999","-9.04299","-0.383802"
"user_002","Standing",1446309440904,"-3.37159","-9.11964","-0.345482"
"user_002","Standing",1446309440912,"-3.33327","-9.04299","-0.460442"
"user_002","Standing",1446309440920,"-3.44823","-8.96635","-0.460442"
"user_002","Standing",1446309440927,"-3.25663","-8.81307","-0.345482"
"user_002","Standing",1446309440936,"-3.25663","-8.92803","-0.460442"
"user_002","Standing",1446309440944,"-3.40991","-8.85139","-0.652044"
"user_002","Standing",1446309440951,"-3.21831","-8.85139","-0.575403"
"user_002","Standing",1446309440960,"-3.33327","-8.85139","-0.498763"
"user_002","Standing",1446309440968,"-3.37159","-8.85139","-0.460442"
"user_002","Standing",1446309440976,"-3.17999","-8.81307","-0.728685"
"user_002","Standing",1446309440985,"-2.98839","-8.88971","-0.575403"
"user_002","Standing",1446309440992,"-3.06503","-8.85139","-0.613724"
"user_002","S...
Distributed Vertex Programming using GraphX
This is an augmentation of http://go.databricks.com/hubfs/notebooks/3-GraphFrames-User-Guide-scala.html that was last retrieved in 2019.
See:
- https://amplab.cs.berkeley.edu/wp-content/uploads/2014/09/graphx.pdf
- https://amplab.github.io/graphx/
- https://spark.apache.org/docs/latest/graphx-programming-guide.html
- https://databricks.com/blog/2016/03/03/introducing-graphframes.html
- https://databricks.com/blog/2016/03/16/on-time-flight-performance-with-spark-graphframes.html
- http://ampcamp.berkeley.edu/big-data-mini-course/graph-analytics-with-graphx.html
And of course the databricks guide: * https://docs.databricks.com/spark/latest/graph-analysis/index.html
Use the source, Luke/Lea!
Community Packages in Spark - more generally
Let us recall the following quoate in Chapter 10 of High Performance Spark book (needs access to Orielly publishers via your library/subscription): - https://learning.oreilly.com/library/view/high-performance-spark/9781491943199/ch10.html#components
Beyond the integrated components, the community packages can add important functionality to Spark, sometimes even superseding built-in functionality—like with GraphFrames.
Here we introduce you to GraphFrames quickly so you don't need to drop down to the GraphX library that requires more understanding of caching and checkpointing to keep the vertex program's DAG from exploding or becoming inefficient.
GraphFrames User Guide (Scala)
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs. It provides high-level APIs in Scala, Java, and Python. It aims to provide both the functionality of GraphX and extended functionality taking advantage of Spark DataFrames. This extended functionality includes motif finding, DataFrame-based serialization, and highly expressive graph queries.
The GraphFrames package is available from Spark Packages.
This notebook demonstrates examples from the GraphFrames User Guide: https://graphframes.github.io/graphframes/docs/_site/user-guide.html.
sc.version // link the right library depending on Spark version of the cluster that's running
// spark version 2.3.0 works with graphframes:graphframes:0.7.0-spark2.3-s_2.11
// spark version 3.0.1 works with graphframes:graphframes:0.8.1-spark3.0-s_2.12
res0: String = 3.1.2
Since databricks.com stopped allowing IFrame embeds we have to open it in a separate window now. The blog is insightful and worth a perusal:
- https://databricks.com/blog/2016/03/03/introducing-graphframes.html
// we first need to install the library - graphframes as a Spark package - and attach it to our cluster - see note two cells above!
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
Creating GraphFrames
Let us try to create an example social network from the blog: * https://databricks.com/blog/2016/03/03/introducing-graphframes.html.

Users can create GraphFrames from vertex and edge DataFrames.
- Vertex DataFrame: A vertex DataFrame should contain a special column named
idwhich specifies unique IDs for each vertex in the graph. - Edge DataFrame: An edge DataFrame should contain two special columns:
src(source vertex ID of edge) anddst(destination vertex ID of edge).
Both DataFrames can have arbitrary other columns. Those columns can represent vertex and edge attributes.
In our example, we can use a GraphFrame can store data or properties associated with each vertex and edge.
In our social network, each user might have an age and name, and each connection might have a relationship type.
Create the vertices and edges
// Vertex DataFrame
val v = sqlContext.createDataFrame(List(
("a", "Alice", 34),
("b", "Bob", 36),
("c", "Charlie", 30),
("d", "David", 29),
("e", "Esther", 32),
("f", "Fanny", 36),
("g", "Gabby", 60)
)).toDF("id", "name", "age")
// Edge DataFrame
val e = sqlContext.createDataFrame(List(
("a", "b", "friend"),
("b", "c", "follow"),
("c", "b", "follow"),
("f", "c", "follow"),
("e", "f", "follow"),
("e", "d", "friend"),
("d", "a", "friend"),
("a", "e", "friend")
)).toDF("src", "dst", "relationship")
v: org.apache.spark.sql.DataFrame = [id: string, name: string ... 1 more field]
e: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
Let's create a graph from these vertices and these edges:
val g = GraphFrame(v, e)
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
Let's use the d3.graphs to visualise graphs (recall the D3 graphs in wiki-click example). You need the Run Cell below using that cell's Play button's drop-down menu.
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3.graphs.help()
Produces a force-directed graph given a collection of edges of the following form: case class Edge(src: String, dest: String, count: Long)
Usage:
import d3._
graphs.force(
height = 500,
width = 500,
clicks: Dataset[Edge])
import org.apache.spark.sql.functions.lit // import the lit function in sql
val gE= g.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")) // for us the column count is just an edge incidence
import org.apache.spark.sql.functions.lit
gE: org.apache.spark.sql.DataFrame = [src: string, dest: string ... 1 more field]
display(gE)
| src | dest | count |
|---|---|---|
| a | b | 1.0 |
| b | c | 1.0 |
| c | b | 1.0 |
| f | c | 1.0 |
| e | f | 1.0 |
| e | d | 1.0 |
| d | a | 1.0 |
| a | e | 1.0 |
d3.graphs.force(
height = 500,
width = 500,
clicks = gE.as[d3.Edge])
// This example graph also comes with the GraphFrames package.
val g0 = examples.Graphs.friends
g0: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
d3.graphs.force( // let us see g0 now in one cell
height = 500,
width = 500,
clicks = g0.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])
Basic graph and DataFrame queries
GraphFrames provide several simple graph queries, such as node degree.
Also, since GraphFrames represent graphs as pairs of vertex and edge DataFrames, it is easy to make powerful queries directly on the vertex and edge DataFrames. Those DataFrames are made available as vertices and edges fields in the GraphFrame.
Simple queries are simple
GraphFrames make it easy to express queries over graphs. Since GraphFrame vertices and edges are stored as DataFrames, many queries are just DataFrame (or SQL) queries.
display(g.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g0.vertices) // this is the same query on the graph loaded as an example from GraphFrame package
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g.edges)
| src | dst | relationship |
|---|---|---|
| a | b | friend |
| b | c | follow |
| c | b | follow |
| f | c | follow |
| e | f | follow |
| e | d | friend |
| d | a | friend |
| a | e | friend |
The incoming degree of the vertices:
display(g.inDegrees)
| id | inDegree |
|---|---|
| c | 2.0 |
| d | 1.0 |
| e | 1.0 |
| b | 2.0 |
| f | 1.0 |
| a | 1.0 |
The outgoing degree of the vertices:
display(g.outDegrees)
| id | outDegree |
|---|---|
| a | 2.0 |
| c | 1.0 |
| e | 2.0 |
| d | 1.0 |
| b | 1.0 |
| f | 1.0 |
The degree of the vertices:
display(g.degrees)
| id | degree |
|---|---|
| b | 3.0 |
| a | 3.0 |
| c | 3.0 |
| f | 2.0 |
| e | 3.0 |
| d | 2.0 |
You can run queries directly on the vertices DataFrame. For example, we can find the age of the youngest person in the graph:
val youngest = g.vertices.groupBy().min("age")
display(youngest)
| min(age) |
|---|
| 29.0 |
Likewise, you can run queries on the edges DataFrame.
For example, let us count the number of 'follow' relationships in the graph:
val numFollows = g.edges.filter("relationship = 'follow'").count()
numFollows: Long = 4
Motif finding
More complex relationships involving edges and vertices can be built using motifs.
The following cell finds the pairs of vertices with edges in both directions between them.
The result is a dataframe, in which the column names are given by the motif keys.
Check out the GraphFrame User Guide at https://graphframes.github.io/graphframes/docs/_site/user-guide.html for more details on the API.
// Search for pairs of vertices with edges in both directions between them, i.e., find undirected or bidirected edges.
val motifs = g.find("(a)-[e1]->(b); (b)-[e2]->(a)")
display(motifs)
Since the result is a DataFrame, more complex queries can be built on top of the motif.
Let us find all the reciprocal relationships in which one person is older than 30:
val filtered = motifs.filter("b.age > 30")
display(filtered)
You Try!
//Search for all "directed triangles" or triplets of vertices: a,b,c with edges: a->b, b->c and c->a
//uncomment the next 2 lines and replace the "XXX" below
//val motifs3 = g.find("(a)-[e1]->(b); (b)-[e2]->(c); (c)-[e3]->(XXX)")
//display(motifs3)
Stateful queries
Many motif queries are stateless and simple to express, as in the examples above. The next examples demonstrate more complex queries which carry state along a path in the motif. These queries can be expressed by combining GraphFrame motif finding with filters on the result, where the filters use sequence operations to construct a series of DataFrame Columns.
For example, suppose one wishes to identify a chain of 4 vertices with some property defined by a sequence of functions. That is, among chains of 4 vertices a->b->c->d, identify the subset of chains matching this complex filter:
- Initialize state on path.
- Update state based on vertex a.
- Update state based on vertex b.
- Etc. for c and d.
- If final state matches some condition, then the chain is accepted by the filter.
The below code snippets demonstrate this process, where we identify chains of 4 vertices such that at least 2 of the 3 edges are friend relationships. In this example, the state is the current count of friend edges; in general, it could be any DataFrame Column.
// Find chains of 4 vertices.
val chain4 = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")
// Query on sequence, with state (cnt)
// (a) Define method for updating state given the next element of the motif.
def sumFriends(cnt: Column, relationship: Column): Column = {
when(relationship === "friend", cnt + 1).otherwise(cnt)
}
// (b) Use sequence operation to apply method to sequence of elements in motif.
// In this case, the elements are the 3 edges.
val condition = Seq("ab", "bc", "cd").
foldLeft(lit(0))((cnt, e) => sumFriends(cnt, col(e)("relationship")))
// (c) Apply filter to DataFrame.
val chainWith2Friends2 = chain4.where(condition >= 2)
display(chainWith2Friends2)
chain4
res22: org.apache.spark.sql.DataFrame = [a: struct<id: string, name: string ... 1 more field>, ab: struct<src: string, dst: string ... 1 more field> ... 5 more fields]
chain4.printSchema
root
|-- a: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- ab: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- b: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- bc: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- c: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- cd: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- d: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
An idea -- a diatribe into an AI security product.
Can you think of a way to use stateful queries in social media networks to find perpetrators of hate-speech online who are possibly worthy of an investigation by domain experts, say in the intelligence or security domain, for potential prosecution on charges of having incited another person to cause physical violence... This is a real problem today as Swedish law effectively prohibits certain forms of online hate-speech.
An idea for a product that can be used by Swedish security agencies?
See https://näthatsgranskaren.se/ for details of a non-profit in Sweden doing such operaitons mostly manually as of early 2020.
Subgraphs
Subgraphs are built by filtering a subset of edges and vertices. For example, the following subgraph only contains people who are friends and who are more than 30 years old.
// Select subgraph of users older than 30, and edges of type "friend"
val v2 = g.vertices.filter("age > 30")
val e2 = g.edges.filter("relationship = 'friend'")
val g2 = GraphFrame(v2, e2)
v2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, name: string ... 1 more field]
e2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g2.edges)
| src | dst | relationship |
|---|---|---|
| a | b | friend |
| e | d | friend |
| d | a | friend |
| a | e | friend |
d3.graphs.force( // let us see g2 now in one cell
height = 500,
width = 500,
clicks = g2.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])
Complex triplet filters
The following example shows how to select a subgraph based upon triplet filters which operate on:
- an edge and
- its src and
- dst vertices.
This example could be extended to go beyond triplets by using more complex motifs.
// Select subgraph based on edges "e" of type "follow"
// pointing from a younger user "a" to an older user "b".
val paths = g.find("(a)-[e]->(b)")
.filter("e.relationship = 'follow'")
.filter("a.age < b.age")
// "paths" contains vertex info. Extract the edges.
val e2 = paths.select("e.src", "e.dst", "e.relationship")
// In Spark 1.5+, the user may simplify this call:
// val e2 = paths.select("e.*")
// Construct the subgraph
val g2 = GraphFrame(g.vertices, e2)
paths: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: struct<id: string, name: string ... 1 more field>, e: struct<src: string, dst: string ... 1 more field> ... 1 more field]
e2: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g2.edges)
| src | dst | relationship |
|---|---|---|
| c | b | follow |
| e | f | follow |
Standard graph algorithms in GraphX conveniently via GraphFrames
GraphFrames comes with a number of standard graph algorithms built in:
- Breadth-first search (BFS)
- Connected components
- Strongly connected components
- Label Propagation Algorithm (LPA)
- PageRank
- Shortest paths
- Triangle count
Read
https://graphframes.github.io/graphframes/docs/_site/user-guide.html
Search from "Esther" for users of age < 32.
// Search from "Esther" for users of age <= 32.
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32").run()
display(paths)
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther' OR name = 'Bob'").toExpr("age < 32").run()
display(paths)
The search may also be limited by edge filters and maximum path lengths.
val filteredPaths = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32")
.edgeFilter("relationship != 'friend'")
.maxPathLength(3)
.run()
display(filteredPaths)
Connected components
Compute the connected component membership of each vertex and return a graph with each vertex assigned a component ID.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components.
From https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components:-
NOTE: With GraphFrames 0.3.0 and later releases, the default Connected Components algorithm requires setting a Spark checkpoint directory. Users can revert to the old algorithm using .setAlgorithm("graphx").
Recall the following quote from Chapter 5 on Effective Transformations of the High Performance Spark Book why one needs to check-point to keep the RDD lineage DAGs from growing too large.
Types of Reuse: Cache, Persist, Checkpoint, Shuffle Files If you decide that you need to reuse your RDD, Spark provides a multitude of options for how to store the RDD. Thus it is important to understand when to use the various types of persistence.There are three primary operations that you can use to store your RDD: cache, persist, and checkpoint. In general, caching (equivalent to persisting with the in-memory storage) and persisting are most useful to avoid recomputation during one Spark job or to break RDDs with long lineages, since they keep an RDD on the executors during a Spark job. Checkpointing is most useful to prevent failures and a high cost of recomputation by saving intermediate results. Like persisting, checkpointing helps avoid computation, thus minimizing the cost of failure, and avoids recomputation by breaking the lineage graph.
sc.setCheckpointDir("/_checkpoint") // just a directory in distributed file system
val result = g.connectedComponents.run()
display(result)
| id | name | age | component |
|---|---|---|---|
| a | Alice | 34.0 | 4.12316860416e11 |
| b | Bob | 36.0 | 4.12316860416e11 |
| c | Charlie | 30.0 | 4.12316860416e11 |
| d | David | 29.0 | 4.12316860416e11 |
| e | Esther | 32.0 | 4.12316860416e11 |
| f | Fanny | 36.0 | 4.12316860416e11 |
| g | Gabby | 60.0 | 1.46028888064e11 |
Fun Exercise: Try to modify the d3.graph function to allow a visualisation of a given Sequence of component ids in the above result.
Strongly connected components
Compute the strongly connected component (SCC) of each vertex and return a graph with each vertex assigned to the SCC containing that vertex.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#strongly-connected-components.
val result = g.stronglyConnectedComponents.maxIter(10).run()
display(result.orderBy("component"))
| id | name | age | component |
|---|---|---|---|
| g | Gabby | 60.0 | 1.46028888064e11 |
| f | Fanny | 36.0 | 4.12316860416e11 |
| a | Alice | 34.0 | 6.70014898176e11 |
| e | Esther | 32.0 | 6.70014898176e11 |
| d | David | 29.0 | 6.70014898176e11 |
| b | Bob | 36.0 | 1.047972020224e12 |
| c | Charlie | 30.0 | 1.047972020224e12 |
Label propagation
Run static Label Propagation Algorithm for detecting communities in networks.
Each node in the network is initially assigned to its own community. At every superstep, nodes send their community affiliation to all neighbors and update their state to the mode community affiliation of incoming messages.
LPA is a standard community detection algorithm for graphs. It is very inexpensive computationally, although
- (1) convergence is not guaranteed and
- (2) one can end up with trivial solutions (all nodes are identified into a single community).
READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#label-propagation-algorithm-lpa.
val result = g.labelPropagation.maxIter(5).run()
display(result.orderBy("label"))
| id | name | age | label |
|---|---|---|---|
| g | Gabby | 60.0 | 1.46028888064e11 |
| b | Bob | 36.0 | 1.047972020224e12 |
| e | Esther | 32.0 | 1.382979469312e12 |
| a | Alice | 34.0 | 1.382979469312e12 |
| c | Charlie | 30.0 | 1.382979469312e12 |
| f | Fanny | 36.0 | 1.46028888064e12 |
| d | David | 29.0 | 1.46028888064e12 |
PageRank
Identify important vertices in a graph based on connections.
READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#pagerank.
// Run PageRank until convergence to tolerance "tol".
val results = g.pageRank.resetProbability(0.15).tol(0.01).run()
display(results.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 2.655507832863289 |
| e | Esther | 32.0 | 0.37085233187676075 |
| a | Alice | 34.0 | 0.44910633706538744 |
| f | Fanny | 36.0 | 0.3283606792049851 |
| g | Gabby | 60.0 | 0.1799821386239711 |
| d | David | 29.0 | 0.3283606792049851 |
| c | Charlie | 30.0 | 2.6878300011606218 |
display(results.edges)
| src | dst | relationship | weight |
|---|---|---|---|
| f | c | follow | 1.0 |
| e | f | follow | 0.5 |
| e | d | friend | 0.5 |
| d | a | friend | 1.0 |
| c | b | follow | 1.0 |
| b | c | follow | 1.0 |
| a | e | friend | 0.5 |
| a | b | friend | 0.5 |
// Run PageRank for a fixed number of iterations.
val results2 = g.pageRank.resetProbability(0.15).maxIter(10).run()
display(results2.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 2.7025217677349773 |
| e | Esther | 32.0 | 0.3613490987992571 |
| a | Alice | 34.0 | 0.4485115093698443 |
| f | Fanny | 36.0 | 0.32504910549694244 |
| g | Gabby | 60.0 | 0.17073170731707318 |
| d | David | 29.0 | 0.32504910549694244 |
| c | Charlie | 30.0 | 2.6667877057849627 |
// Run PageRank personalized for vertex "a"
val results3 = g.pageRank.resetProbability(0.15).maxIter(10).sourceId("a").run()
display(results3.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 0.3366143039702568 |
| e | Esther | 32.0 | 7.657840357273027e-2 |
| a | Alice | 34.0 | 0.17710831642683564 |
| f | Fanny | 36.0 | 3.189213697274781e-2 |
| g | Gabby | 60.0 | 0.0 |
| d | David | 29.0 | 3.189213697274781e-2 |
| c | Charlie | 30.0 | 0.3459147020846817 |
Shortest paths
Computes shortest paths to the given set of landmark vertices, where landmarks are specified by vertex ID.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#shortest-paths.
val paths = g.shortestPaths.landmarks(Seq("a", "d")).run()
display(paths)
g.edges.show()
+---+---+------------+
|src|dst|relationship|
+---+---+------------+
| a| b| friend|
| b| c| follow|
| c| b| follow|
| f| c| follow|
| e| f| follow|
| e| d| friend|
| d| a| friend|
| a| e| friend|
+---+---+------------+
Triangle count
Computes the number of triangles passing through each vertex.
val results = g.triangleCount.run()
display(results)
| count | id | name | age |
|---|---|---|---|
| 1.0 | a | Alice | 34.0 |
| 0.0 | b | Bob | 36.0 |
| 0.0 | c | Charlie | 30.0 |
| 1.0 | d | David | 29.0 |
| 1.0 | e | Esther | 32.0 |
| 0.0 | f | Fanny | 36.0 |
| 0.0 | g | Gabby | 60.0 |
YouTry
and undestand how the below code snippet shows how to use aggregateMessages to compute the sum of the ages of adjacent users.
import org.graphframes.{examples,GraphFrame}
import org.graphframes.lib.AggregateMessages
val g: GraphFrame = examples.Graphs.friends // get example graph
// We will use AggregateMessages utilities later, so name it "AM" for short.
val AM = AggregateMessages
// For each user, sum the ages of the adjacent users.
val msgToSrc = AM.dst("age")
val msgToDst = AM.src("age")
val agg = { g.aggregateMessages
.sendToSrc(msgToSrc) // send destination user's age to source
.sendToDst(msgToDst) // send source user's age to destination
.agg(sum(AM.msg).as("summedAges")) } // sum up ages, stored in AM.msg column
agg.show()
+---+----------+
| id|summedAges|
+---+----------+
| a| 97|
| c| 108|
| e| 99|
| d| 66|
| b| 94|
| f| 62|
+---+----------+
import org.graphframes.{examples, GraphFrame}
import org.graphframes.lib.AggregateMessages
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
AM: org.graphframes.lib.AggregateMessages.type = org.graphframes.lib.AggregateMessages$@706833c8
msgToSrc: org.apache.spark.sql.Column = dst[age]
msgToDst: org.apache.spark.sql.Column = src[age]
agg: org.apache.spark.sql.DataFrame = [id: string, summedAges: bigint]
There is a lot more that can be done with aggregate messaging - let's get into belief propogation algorithm for a more complex example!
Belief propogation is a powerful computational framework for Graphical Models.
- let's dive here:
as
This provides a template for building customized BP algorithms for different types of graphical models.
Project Idea
Understand parallel belief propagation using colored fields in the Scala code linked above and also pasted below in one cell (for you to modify if you want to do it in a databricks or jupyter or zeppelin notebook) unless you want to fork and extend the github repo directly with your own example.
Then use it with necessary adaptations to be able to model your favorite interacting particle system. Don't just redo the Ising model done there!
This can be used to gain intuition for various real-world scenarios, including the mathematics in your head:
- Make a graph for contact network of a set of hosts
- A simple model of COVID spreading in an SI or SIS or SIR or other epidemic models
- this can be abstract and simply show your skills in programming, say create a random network
- or be more explicit with some assumptions about the contact process (population sampled, in one or two cities, with some assumptions on contacts during transportation, school, work, etc)
- show that you have a fully scalable simulation model that can theoretically scale to billions of hosts
The project does not have to be a recommendation to Swedish authorities! Just a start in the right direction, for instance.
Some readings that can help here include the following and references therein:
- The Transmission Process: A Combinatorial Stochastic Process for the Evolution of Transmission Trees over Networks, Raazesh Sainudiin and David Welch, Journal of Theoretical Biology, Volume 410, Pages 137–170, 10.1016/j.jtbi.2016.07.038, 2016.
Other Project Ideas
- try to do a scalable inference algorithm for one of the graphical models that you already know...
- make a large simulaiton of your favourite Finite Markov Information Exchange (FMIE) process defined by Aldous (see reference in the above linked paper)
- anything else that fancies you or your research orientation/interests and can benefit from adapting the template for the parallel belief propagation algorithm here.
If you want to do this project in databricks (or other) notebook then start by modifying the following code from the example and making it run... Then adapt... start in small steps... make a team with fellow students with complementary skills...
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.graphframes.examples
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx.{Graph, VertexRDD, Edge => GXEdge}
import org.apache.spark.sql.{Column, Row, SparkSession, SQLContext}
import org.apache.spark.sql.functions.{col, lit, sum, udf, when}
import org.graphframes.GraphFrame
import org.graphframes.examples.Graphs.gridIsingModel
import org.graphframes.lib.AggregateMessages
/**
* Example code for Belief Propagation (BP)
*
* This provides a template for building customized BP algorithms for different types of
* graphical models.
*
* This example:
* - Ising model on a grid
* - Parallel Belief Propagation using colored fields
*
* Ising models are probabilistic graphical models over binary variables x,,i,,.
* Each binary variable x,,i,, corresponds to one vertex, and it may take values -1 or +1.
* The probability distribution P(X) (over all x,,i,,) is parameterized by vertex factors a,,i,,
* and edge factors b,,ij,,:
* {{{
* P(X) = (1/Z) * exp[ \sum_i a_i x_i + \sum_{ij} b_{ij} x_i x_j ]
* }}}
* where Z is the normalization constant (partition function).
* See [[https://en.wikipedia.org/wiki/Ising_model Wikipedia]] for more information on Ising models.
*
* Belief Propagation (BP) provides marginal probabilities of the values of the variables x,,i,,,
* i.e., P(x,,i,,) for each i. This allows a user to understand likely values of variables.
* See [[https://en.wikipedia.org/wiki/Belief_propagation Wikipedia]] for more information on BP.
*
* We use a batch synchronous BP algorithm, where batches of vertices are updated synchronously.
* We follow the mean field update algorithm in Slide 13 of the
* [[http://www.eecs.berkeley.edu/~wainwrig/Talks/A_GraphModel_Tutorial talk slides]] from:
* Wainwright. "Graphical models, message-passing algorithms, and convex optimization."
*
* The batches are chosen according to a coloring. For background on graph colorings for inference,
* see for example:
* Gonzalez et al. "Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees."
* AISTATS, 2011.
*
* The BP algorithm works by:
* - Coloring the graph by assigning a color to each vertex such that no neighboring vertices
* share the same color.
* - In each step of BP, update all vertices of a single color. Alternate colors.
*/
object BeliefPropagation {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName("BeliefPropagation example")
.getOrCreate()
val sql = spark.sqlContext
// Create graphical model g of size 3 x 3.
val g = gridIsingModel(sql, 3)
println("Original Ising model:")
g.vertices.show()
g.edges.show()
// Run BP for 5 iterations.
val numIter = 5
val results = runBPwithGraphX(g, numIter)
// Display beliefs.
val beliefs = results.vertices.select("id", "belief")
println(s"Done with BP. Final beliefs after $numIter iterations:")
beliefs.show()
spark.stop()
}
/**
* Given a GraphFrame, choose colors for each vertex. No neighboring vertices will share the
* same color. The number of colors is minimized.
*
* This is written specifically for grid graphs. For non-grid graphs, it should be generalized,
* such as by using a greedy coloring scheme.
*
* @param g Grid graph generated by [[org.graphframes.examples.Graphs.gridIsingModel()]]
* @return Same graph, but with a new vertex column "color" of type Int (0 or 1)
*/
private def colorGraph(g: GraphFrame): GraphFrame = {
val colorUDF = udf { (i: Int, j: Int) => (i + j) % 2 }
val v = g.vertices.withColumn("color", colorUDF(col("i"), col("j")))
GraphFrame(v, g.edges)
}
/**
* Run Belief Propagation.
*
* This implementation of BP shows how to use GraphX's aggregateMessages method.
* It is simple to convert to and from GraphX format. This method does the following:
* - Color GraphFrame vertices for BP scheduling.
* - Convert GraphFrame to GraphX format.
* - Run BP using GraphX's aggregateMessages API.
* - Augment the original GraphFrame with the BP results (vertex beliefs).
*
* @param g Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
* @param numIter Number of iterations of BP to run. One iteration includes updating each
* vertex's belief once.
* @return Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
* "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
* value +1 instead of -1.
*/
def runBPwithGraphX(g: GraphFrame, numIter: Int): GraphFrame = {
// Choose colors for vertices for BP scheduling.
val colorG = colorGraph(g)
val numColors: Int = colorG.vertices.select("color").distinct.count().toInt
// Convert GraphFrame to GraphX, and initialize beliefs.
val gx0 = colorG.toGraphX
// Schema maps for extracting attributes
val vColsMap = colorG.vertexColumnMap
val eColsMap = colorG.edgeColumnMap
// Convert vertex attributes to nice case classes.
val gx1: Graph[VertexAttr, Row] = gx0.mapVertices { case (_, attr) =>
// Initialize belief at 0.0
VertexAttr(attr.getDouble(vColsMap("a")), 0.0, attr.getInt(vColsMap("color")))
}
// Convert edge attributes to nice case classes.
val extractEdgeAttr: (GXEdge[Row] => EdgeAttr) = { e =>
EdgeAttr(e.attr.getDouble(eColsMap("b")))
}
var gx: Graph[VertexAttr, EdgeAttr] = gx1.mapEdges(extractEdgeAttr)
// Run BP for numIter iterations.
for (iter <- Range(0, numIter)) {
// For each color, have that color receive messages from neighbors.
for (color <- Range(0, numColors)) {
// Send messages to vertices of the current color.
val msgs: VertexRDD[Double] = gx.aggregateMessages(
ctx =>
// Can send to source or destination since edges are treated as undirected.
if (ctx.dstAttr.color == color) {
val msg = ctx.attr.b * ctx.srcAttr.belief
// Only send message if non-zero.
if (msg != 0) ctx.sendToDst(msg)
} else if (ctx.srcAttr.color == color) {
val msg = ctx.attr.b * ctx.dstAttr.belief
// Only send message if non-zero.
if (msg != 0) ctx.sendToSrc(msg)
},
_ + _)
// Receive messages, and update beliefs for vertices of the current color.
gx = gx.outerJoinVertices(msgs) {
case (vID, vAttr, optMsg) =>
if (vAttr.color == color) {
val x = vAttr.a + optMsg.getOrElse(0.0)
val newBelief = math.exp(-log1pExp(-x))
VertexAttr(vAttr.a, newBelief, color)
} else {
vAttr
}
}
}
}
// Convert back to GraphFrame with a new column "belief" for vertices DataFrame.
val gxFinal: Graph[Double, Unit] = gx.mapVertices((_, attr) => attr.belief).mapEdges(_ => ())
GraphFrame.fromGraphX(colorG, gxFinal, vertexNames = Seq("belief"))
}
case class VertexAttr(a: Double, belief: Double, color: Int)
case class EdgeAttr(b: Double)
/**
* Run Belief Propagation.
*
* This implementation of BP shows how to use GraphFrame's aggregateMessages method.
* - Color GraphFrame vertices for BP scheduling.
* - Run BP using GraphFrame's aggregateMessages API.
* - Augment the original GraphFrame with the BP results (vertex beliefs).
*
* @param g Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
* @param numIter Number of iterations of BP to run. One iteration includes updating each
* vertex's belief once.
* @return Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
* "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
* value +1 instead of -1.
*/
def runBPwithGraphFrames(g: GraphFrame, numIter: Int): GraphFrame = {
// Choose colors for vertices for BP scheduling.
val colorG = colorGraph(g)
val numColors: Int = colorG.vertices.select("color").distinct.count().toInt
// TODO: Handle vertices without any edges.
// Initialize vertex beliefs at 0.0.
var gx = GraphFrame(colorG.vertices.withColumn("belief", lit(0.0)), colorG.edges)
// Run BP for numIter iterations.
for (iter <- Range(0, numIter)) {
// For each color, have that color receive messages from neighbors.
for (color <- Range(0, numColors)) {
// Define "AM" for shorthand for referring to the src, dst, edge, and msg fields.
// (See usage below.)
val AM = AggregateMessages
// Send messages to vertices of the current color.
// We may send to source or destination since edges are treated as undirected.
val msgForSrc: Column = when(AM.src("color") === color, AM.edge("b") * AM.dst("belief"))
val msgForDst: Column = when(AM.dst("color") === color, AM.edge("b") * AM.src("belief"))
val logistic = udf { (x: Double) => math.exp(-log1pExp(-x)) }
val aggregates = gx.aggregateMessages
.sendToSrc(msgForSrc)
.sendToDst(msgForDst)
.agg(sum(AM.msg).as("aggMess"))
val v = gx.vertices
// Receive messages, and update beliefs for vertices of the current color.
val newBeliefCol = when(v("color") === color && aggregates("aggMess").isNotNull,
logistic(aggregates("aggMess") + v("a")))
.otherwise(v("belief")) // keep old beliefs for other colors
val newVertices = v
.join(aggregates, v("id") === aggregates("id"), "left_outer") // join messages, vertices
.drop(aggregates("id")) // drop duplicate ID column (from outer join)
.withColumn("newBelief", newBeliefCol) // compute new beliefs
.drop("aggMess") // drop messages
.drop("belief") // drop old beliefs
.withColumnRenamed("newBelief", "belief")
// Cache new vertices using workaround for SPARK-13346
val cachedNewVertices = AM.getCachedDataFrame(newVertices)
gx = GraphFrame(cachedNewVertices, gx.edges)
}
}
// Drop the "color" column from vertices
GraphFrame(gx.vertices.drop("color"), gx.edges)
}
/** More numerically stable `log(1 + exp(x))` */
private def log1pExp(x: Double): Double = {
if (x > 0) {
x + math.log1p(math.exp(-x))
} else {
math.log1p(math.exp(x))
}
}
}
This is a scala version of the python notebook in the following talk:
Homework:
See https://www.brighttalk.com/webcast/12891/199003 (you need to subscribe freely to Bright Talk first). Then go through this scala version of the notebook from the talk.
On-Time Flight Performance with GraphFrames for Apache Spark
This notebook provides an analysis of On-Time Flight Performance and Departure Delays data using GraphFrames for Apache Spark.
Source Data:
- OpenFlights: Airport, airline and route data
- United States Department of Transportation: Bureau of Transportation Statistics (TranStats)
- Note, the data used here was extracted from the US DOT:BTS between 1/1/2014 and 3/31/2014*
References:
Preparation
Extract the Airports and Departure Delays information from S3 / DBFS
// Set File Paths
val tripdelaysFilePath = "/databricks-datasets/flights/departuredelays.csv"
val airportsnaFilePath = "/databricks-datasets/flights/airport-codes-na.txt"
tripdelaysFilePath: String = /databricks-datasets/flights/departuredelays.csv
airportsnaFilePath: String = /databricks-datasets/flights/airport-codes-na.txt
// Obtain airports dataset
// Note that "spark-csv" package is built-in datasource in Spark 2.0
val airportsna = sqlContext.read.format("com.databricks.spark.csv").
option("header", "true").
option("inferschema", "true").
option("delimiter", "\t").
load(airportsnaFilePath)
airportsna.createOrReplaceTempView("airports_na")
// Obtain departure Delays data
val departureDelays = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load(tripdelaysFilePath)
departureDelays.createOrReplaceTempView("departureDelays")
departureDelays.cache()
// Available IATA (International Air Transport Association) codes from the departuredelays sample dataset
val tripIATA = sqlContext.sql("select distinct iata from (select distinct origin as iata from departureDelays union all select distinct destination as iata from departureDelays) a")
tripIATA.createOrReplaceTempView("tripIATA")
// Only include airports with atleast one trip from the departureDelays dataset
val airports = sqlContext.sql("select f.IATA, f.City, f.State, f.Country from airports_na f join tripIATA t on t.IATA = f.IATA")
airports.createOrReplaceTempView("airports")
airports.cache()
airportsna: org.apache.spark.sql.DataFrame = [City: string, State: string ... 2 more fields]
departureDelays: org.apache.spark.sql.DataFrame = [date: string, delay: string ... 3 more fields]
tripIATA: org.apache.spark.sql.DataFrame = [iata: string]
airports: org.apache.spark.sql.DataFrame = [IATA: string, City: string ... 2 more fields]
res0: airports.type = [IATA: string, City: string ... 2 more fields]
// Build `departureDelays_geo` DataFrame
// Obtain key attributes such as Date of flight, delays, distance, and airport information (Origin, Destination)
val departureDelays_geo = sqlContext.sql("select cast(f.date as int) as tripid, cast(concat(concat(concat(concat(concat(concat('2014-', concat(concat(substr(cast(f.date as string), 1, 2), '-')), substr(cast(f.date as string), 3, 2)), ' '), substr(cast(f.date as string), 5, 2)), ':'), substr(cast(f.date as string), 7, 2)), ':00') as timestamp) as `localdate`, cast(f.delay as int), cast(f.distance as int), f.origin as src, f.destination as dst, o.city as city_src, d.city as city_dst, o.state as state_src, d.state as state_dst from departuredelays f join airports o on o.iata = f.origin join airports d on d.iata = f.destination")
// RegisterTempTable
departureDelays_geo.createOrReplaceTempView("departureDelays_geo")
// Cache and Count
departureDelays_geo.cache()
departureDelays_geo.count()
departureDelays_geo: org.apache.spark.sql.DataFrame = [tripid: int, localdate: timestamp ... 8 more fields]
res2: Long = 1361141
display(departureDelays_geo)
| tripid | localdate | delay | distance | src | dst | city_src | city_dst | state_src | state_dst |
|---|---|---|---|---|---|---|---|---|---|
| 1011111.0 | 2014-01-01T11:11:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1021111.0 | 2014-01-02T11:11:00.000+0000 | 7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1031111.0 | 2014-01-03T11:11:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1041925.0 | 2014-01-04T19:25:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1061115.0 | 2014-01-06T11:15:00.000+0000 | 33.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1071115.0 | 2014-01-07T11:15:00.000+0000 | 23.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1081115.0 | 2014-01-08T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1091115.0 | 2014-01-09T11:15:00.000+0000 | 11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1101115.0 | 2014-01-10T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1112015.0 | 2014-01-11T20:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1121925.0 | 2014-01-12T19:25:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1131115.0 | 2014-01-13T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1141115.0 | 2014-01-14T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1151115.0 | 2014-01-15T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1161115.0 | 2014-01-16T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1171115.0 | 2014-01-17T11:15:00.000+0000 | 4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1182015.0 | 2014-01-18T20:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1191925.0 | 2014-01-19T19:25:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1201115.0 | 2014-01-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1211115.0 | 2014-01-21T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1221115.0 | 2014-01-22T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1231115.0 | 2014-01-23T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1241115.0 | 2014-01-24T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1252015.0 | 2014-01-25T20:15:00.000+0000 | -12.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1261925.0 | 2014-01-26T19:25:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1271115.0 | 2014-01-27T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1281115.0 | 2014-01-28T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1291115.0 | 2014-01-29T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1301115.0 | 2014-01-30T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1311115.0 | 2014-01-31T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2012015.0 | 2014-02-01T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2022015.0 | 2014-02-02T20:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2031115.0 | 2014-02-03T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2041115.0 | 2014-02-04T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2051115.0 | 2014-02-05T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2061115.0 | 2014-02-06T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2071115.0 | 2014-02-07T11:15:00.000+0000 | -15.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2082015.0 | 2014-02-08T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2091925.0 | 2014-02-09T19:25:00.000+0000 | 1.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2101115.0 | 2014-02-10T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2111115.0 | 2014-02-11T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2121115.0 | 2014-02-12T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2131115.0 | 2014-02-13T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2141115.0 | 2014-02-14T11:15:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2152015.0 | 2014-02-15T20:15:00.000+0000 | 16.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2161925.0 | 2014-02-16T19:25:00.000+0000 | 169.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2171115.0 | 2014-02-17T11:15:00.000+0000 | 27.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2181115.0 | 2014-02-18T11:15:00.000+0000 | 96.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2191115.0 | 2014-02-19T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2201115.0 | 2014-02-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2211115.0 | 2014-02-21T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2222015.0 | 2014-02-22T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2231925.0 | 2014-02-23T19:25:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2241115.0 | 2014-02-24T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2251115.0 | 2014-02-25T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2261115.0 | 2014-02-26T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2271115.0 | 2014-02-27T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2281115.0 | 2014-02-28T11:15:00.000+0000 | 5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3012015.0 | 2014-03-01T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3022000.0 | 2014-03-02T20:00:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3031115.0 | 2014-03-03T11:15:00.000+0000 | 17.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3041115.0 | 2014-03-04T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3051115.0 | 2014-03-05T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3061115.0 | 2014-03-06T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3071115.0 | 2014-03-07T11:15:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3082000.0 | 2014-03-08T20:00:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3092000.0 | 2014-03-09T20:00:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3101115.0 | 2014-03-10T11:15:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3111115.0 | 2014-03-11T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3121115.0 | 2014-03-12T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3131115.0 | 2014-03-13T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3141115.0 | 2014-03-14T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3152000.0 | 2014-03-15T20:00:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3162000.0 | 2014-03-16T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3171115.0 | 2014-03-17T11:15:00.000+0000 | 25.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3181115.0 | 2014-03-18T11:15:00.000+0000 | 2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3191115.0 | 2014-03-19T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3201115.0 | 2014-03-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3211115.0 | 2014-03-21T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3222000.0 | 2014-03-22T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3232000.0 | 2014-03-23T20:00:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3241115.0 | 2014-03-24T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3251115.0 | 2014-03-25T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3261115.0 | 2014-03-26T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3271115.0 | 2014-03-27T11:15:00.000+0000 | 9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3281115.0 | 2014-03-28T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3292000.0 | 2014-03-29T20:00:00.000+0000 | -19.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3302000.0 | 2014-03-30T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3311115.0 | 2014-03-31T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2011230.0 | 2014-02-01T12:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2010719.0 | 2014-02-01T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021230.0 | 2014-02-02T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2020709.0 | 2014-02-02T07:09:00.000+0000 | 59.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021654.0 | 2014-02-02T16:54:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2030719.0 | 2014-02-03T07:19:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031659.0 | 2014-02-03T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031230.0 | 2014-02-03T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2032043.0 | 2014-02-03T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041230.0 | 2014-02-04T12:30:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2040719.0 | 2014-02-04T07:19:00.000+0000 | 168.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041730.0 | 2014-02-04T17:30:00.000+0000 | 88.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2042043.0 | 2014-02-04T20:43:00.000+0000 | 106.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051659.0 | 2014-02-05T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2050719.0 | 2014-02-05T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2050929.0 | 2014-02-05T09:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2052043.0 | 2014-02-05T20:43:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2060719.0 | 2014-02-06T07:19:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061659.0 | 2014-02-06T16:59:00.000+0000 | 82.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061230.0 | 2014-02-06T12:30:00.000+0000 | 61.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2062043.0 | 2014-02-06T20:43:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2070719.0 | 2014-02-07T07:19:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071659.0 | 2014-02-07T16:59:00.000+0000 | 19.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071230.0 | 2014-02-07T12:30:00.000+0000 | 27.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2072048.0 | 2014-02-07T20:48:00.000+0000 | 47.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2081230.0 | 2014-02-08T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2080719.0 | 2014-02-08T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091229.0 | 2014-02-09T12:29:00.000+0000 | 95.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091654.0 | 2014-02-09T16:54:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2090709.0 | 2014-02-09T07:09:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2092043.0 | 2014-02-09T20:43:00.000+0000 | 32.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2100719.0 | 2014-02-10T07:19:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101659.0 | 2014-02-10T16:59:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101230.0 | 2014-02-10T12:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2102043.0 | 2014-02-10T20:43:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111230.0 | 2014-02-11T12:30:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2110719.0 | 2014-02-11T07:19:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111730.0 | 2014-02-11T17:30:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2112043.0 | 2014-02-11T20:43:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2120719.0 | 2014-02-12T07:19:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2121230.0 | 2014-02-12T12:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2120929.0 | 2014-02-12T09:29:00.000+0000 | 89.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2122043.0 | 2014-02-12T20:43:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2130738.0 | 2014-02-13T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2132041.0 | 2014-02-13T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2140729.0 | 2014-02-14T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2142041.0 | 2014-02-14T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151206.0 | 2014-02-15T12:06:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2150659.0 | 2014-02-15T06:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2160705.0 | 2014-02-16T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2170729.0 | 2014-02-17T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2172041.0 | 2014-02-17T20:41:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2180738.0 | 2014-02-18T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2182041.0 | 2014-02-18T20:41:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2190727.0 | 2014-02-19T07:27:00.000+0000 | 15.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2200738.0 | 2014-02-20T07:38:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2202041.0 | 2014-02-20T20:41:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2210729.0 | 2014-02-21T07:29:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2212041.0 | 2014-02-21T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221206.0 | 2014-02-22T12:06:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2220659.0 | 2014-02-22T06:59:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2232041.0 | 2014-02-23T20:41:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2230705.0 | 2014-02-23T07:05:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2240729.0 | 2014-02-24T07:29:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2242041.0 | 2014-02-24T20:41:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2250738.0 | 2014-02-25T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2260727.0 | 2014-02-26T07:27:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2262041.0 | 2014-02-26T20:41:00.000+0000 | 174.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2270738.0 | 2014-02-27T07:38:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2272041.0 | 2014-02-27T20:41:00.000+0000 | 32.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2280729.0 | 2014-02-28T07:29:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2282041.0 | 2014-02-28T20:41:00.000+0000 | 49.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281000.0 | 2014-02-28T10:00:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051230.0 | 2014-02-05T12:30:00.000+0000 | 216.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2131536.0 | 2014-02-13T15:36:00.000+0000 | 273.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2141536.0 | 2014-02-14T15:36:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151902.0 | 2014-02-15T19:02:00.000+0000 | 31.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151536.0 | 2014-02-15T15:36:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2162041.0 | 2014-02-16T20:41:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2161536.0 | 2014-02-16T15:36:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2171536.0 | 2014-02-17T15:36:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2181536.0 | 2014-02-18T15:36:00.000+0000 | 26.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2191536.0 | 2014-02-19T15:36:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2201536.0 | 2014-02-20T15:36:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2211536.0 | 2014-02-21T15:36:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221900.0 | 2014-02-22T19:00:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221536.0 | 2014-02-22T15:36:00.000+0000 | 65.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2231536.0 | 2014-02-23T15:36:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2241536.0 | 2014-02-24T15:36:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2251536.0 | 2014-02-25T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2261536.0 | 2014-02-26T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2271536.0 | 2014-02-27T15:36:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281536.0 | 2014-02-28T15:36:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2010730.0 | 2014-02-01T07:30:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021815.0 | 2014-02-02T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031815.0 | 2014-02-03T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041815.0 | 2014-02-04T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051815.0 | 2014-02-05T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061815.0 | 2014-02-06T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071815.0 | 2014-02-07T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2080730.0 | 2014-02-08T07:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091815.0 | 2014-02-09T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101815.0 | 2014-02-10T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111815.0 | 2014-02-11T18:15:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2121815.0 | 2014-02-12T18:15:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2131805.0 | 2014-02-13T18:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2141635.0 | 2014-02-14T16:35:00.000+0000 | 52.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2150730.0 | 2014-02-15T07:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2161635.0 | 2014-02-16T16:35:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2171635.0 | 2014-02-17T16:35:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2181635.0 | 2014-02-18T16:35:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2191635.0 | 2014-02-19T16:35:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2201635.0 | 2014-02-20T16:35:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2211635.0 | 2014-02-21T16:35:00.000+0000 | 292.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2220730.0 | 2014-02-22T07:30:00.000+0000 | 28.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2231635.0 | 2014-02-23T16:35:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2241635.0 | 2014-02-24T16:35:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2251635.0 | 2014-02-25T16:35:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2261635.0 | 2014-02-26T16:35:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2271635.0 | 2014-02-27T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281635.0 | 2014-02-28T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011206.0 | 2014-03-01T12:06:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3010659.0 | 2014-03-01T06:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3022041.0 | 2014-03-02T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3020705.0 | 2014-03-02T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3030729.0 | 2014-03-03T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3032041.0 | 2014-03-03T20:41:00.000+0000 | 113.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3040738.0 | 2014-03-04T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3050727.0 | 2014-03-05T07:27:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3052041.0 | 2014-03-05T20:41:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3060705.0 | 2014-03-06T07:05:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3061252.0 | 2014-03-06T12:52:00.000+0000 | 67.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3062100.0 | 2014-03-06T21:00:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3070705.0 | 2014-03-07T07:05:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3071252.0 | 2014-03-07T12:52:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3072100.0 | 2014-03-07T21:00:00.000+0000 | 13.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3080705.0 | 2014-03-08T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3081250.0 | 2014-03-08T12:50:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3091255.0 | 2014-03-09T12:55:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3092059.0 | 2014-03-09T20:59:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3100705.0 | 2014-03-10T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3101252.0 | 2014-03-10T12:52:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3102059.0 | 2014-03-10T20:59:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3112059.0 | 2014-03-11T20:59:00.000+0000 | 181.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3111252.0 | 2014-03-11T12:52:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3122059.0 | 2014-03-12T20:59:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3121252.0 | 2014-03-12T12:52:00.000+0000 | 161.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3130705.0 | 2014-03-13T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3131252.0 | 2014-03-13T12:52:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3132059.0 | 2014-03-13T20:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3140705.0 | 2014-03-14T07:05:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3141252.0 | 2014-03-14T12:52:00.000+0000 | 39.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3142059.0 | 2014-03-14T20:59:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3150700.0 | 2014-03-15T07:00:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3151250.0 | 2014-03-15T12:50:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3161255.0 | 2014-03-16T12:55:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3162059.0 | 2014-03-16T20:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3170705.0 | 2014-03-17T07:05:00.000+0000 | -11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3171252.0 | 2014-03-17T12:52:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3172059.0 | 2014-03-17T20:59:00.000+0000 | 132.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3182059.0 | 2014-03-18T20:59:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3181252.0 | 2014-03-18T12:52:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3192059.0 | 2014-03-19T20:59:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3191252.0 | 2014-03-19T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3200705.0 | 2014-03-20T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3201252.0 | 2014-03-20T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3202059.0 | 2014-03-20T20:59:00.000+0000 | 77.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3210705.0 | 2014-03-21T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3211252.0 | 2014-03-21T12:52:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3212059.0 | 2014-03-21T20:59:00.000+0000 | 11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3220705.0 | 2014-03-22T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3221250.0 | 2014-03-22T12:50:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3221600.0 | 2014-03-22T16:00:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3231255.0 | 2014-03-23T12:55:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3240705.0 | 2014-03-24T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3241252.0 | 2014-03-24T12:52:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3242059.0 | 2014-03-24T20:59:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3252059.0 | 2014-03-25T20:59:00.000+0000 | 121.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3251252.0 | 2014-03-25T12:52:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3262059.0 | 2014-03-26T20:59:00.000+0000 | 49.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3261252.0 | 2014-03-26T12:52:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3270705.0 | 2014-03-27T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3271252.0 | 2014-03-27T12:52:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3272059.0 | 2014-03-27T20:59:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3280705.0 | 2014-03-28T07:05:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3281252.0 | 2014-03-28T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3282059.0 | 2014-03-28T20:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3291250.0 | 2014-03-29T12:50:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3290705.0 | 2014-03-29T07:05:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3291600.0 | 2014-03-29T16:00:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3301255.0 | 2014-03-30T12:55:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3302059.0 | 2014-03-30T20:59:00.000+0000 | 166.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3310705.0 | 2014-03-31T07:05:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3311252.0 | 2014-03-31T12:52:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3312059.0 | 2014-03-31T20:59:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011902.0 | 2014-03-01T19:02:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011536.0 | 2014-03-01T15:36:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3021536.0 | 2014-03-02T15:36:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3031536.0 | 2014-03-03T15:36:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3041536.0 | 2014-03-04T15:36:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3051536.0 | 2014-03-05T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3090715.0 | 2014-03-09T07:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3110705.0 | 2014-03-11T07:05:00.000+0000 | -11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3120659.0 | 2014-03-12T06:59:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3160715.0 | 2014-03-16T07:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3180705.0 | 2014-03-18T07:05:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3190659.0 | 2014-03-19T06:59:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3230715.0 | 2014-03-23T07:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3250705.0 | 2014-03-25T07:05:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3260659.0 | 2014-03-26T06:59:00.000+0000 | 63.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3300715.0 | 2014-03-30T07:15:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3010730.0 | 2014-03-01T07:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3021635.0 | 2014-03-02T16:35:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3031635.0 | 2014-03-03T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3041635.0 | 2014-03-04T16:35:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3051635.0 | 2014-03-05T16:35:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3061635.0 | 2014-03-06T16:35:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3071635.0 | 2014-03-07T16:35:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3080730.0 | 2014-03-08T07:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3091825.0 | 2014-03-09T18:25:00.000+0000 | 45.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3101825.0 | 2014-03-10T18:25:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3111825.0 | 2014-03-11T18:25:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3121825.0 | 2014-03-12T18:25:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3131825.0 | 2014-03-13T18:25:00.000+0000 | 123.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3141825.0 | 2014-03-14T18:25:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3150730.0 | 2014-03-15T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3161825.0 | 2014-03-16T18:25:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3171825.0 | 2014-03-17T18:25:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3181825.0 | 2014-03-18T18:25:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3191825.0 | 2014-03-19T18:25:00.000+0000 | 223.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3201825.0 | 2014-03-20T18:25:00.000+0000 | 178.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3211825.0 | 2014-03-21T18:25:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3220730.0 | 2014-03-22T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3231825.0 | 2014-03-23T18:25:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3241825.0 | 2014-03-24T18:25:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3251825.0 | 2014-03-25T18:25:00.000+0000 | 222.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3261825.0 | 2014-03-26T18:25:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3271825.0 | 2014-03-27T18:25:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3281825.0 | 2014-03-28T18:25:00.000+0000 | 26.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3290730.0 | 2014-03-29T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3301825.0 | 2014-03-30T18:25:00.000+0000 | 139.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3311825.0 | 2014-03-31T18:25:00.000+0000 | 25.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1020705.0 | 2014-01-02T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1030705.0 | 2014-01-03T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1040655.0 | 2014-01-04T06:55:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1050703.0 | 2014-01-05T07:03:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1060705.0 | 2014-01-06T07:05:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071230.0 | 2014-01-07T12:30:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1070719.0 | 2014-01-07T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071730.0 | 2014-01-07T17:30:00.000+0000 | 161.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1072043.0 | 2014-01-07T20:43:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1080719.0 | 2014-01-08T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081659.0 | 2014-01-08T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081230.0 | 2014-01-08T12:30:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1082043.0 | 2014-01-08T20:43:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1090719.0 | 2014-01-09T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091659.0 | 2014-01-09T16:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091230.0 | 2014-01-09T12:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1092043.0 | 2014-01-09T20:43:00.000+0000 | 63.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1100719.0 | 2014-01-10T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101659.0 | 2014-01-10T16:59:00.000+0000 | 244.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101230.0 | 2014-01-10T12:30:00.000+0000 | 110.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1102043.0 | 2014-01-10T20:43:00.000+0000 | 43.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1111230.0 | 2014-01-11T12:30:00.000+0000 | 87.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1110719.0 | 2014-01-11T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121654.0 | 2014-01-12T16:54:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121230.0 | 2014-01-12T12:30:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1120709.0 | 2014-01-12T07:09:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1122043.0 | 2014-01-12T20:43:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1130719.0 | 2014-01-13T07:19:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131659.0 | 2014-01-13T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131230.0 | 2014-01-13T12:30:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1132043.0 | 2014-01-13T20:43:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141230.0 | 2014-01-14T12:30:00.000+0000 | 30.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1140719.0 | 2014-01-14T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141730.0 | 2014-01-14T17:30:00.000+0000 | 69.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1142043.0 | 2014-01-14T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1150719.0 | 2014-01-15T07:19:00.000+0000 | 42.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151659.0 | 2014-01-15T16:59:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151230.0 | 2014-01-15T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1152043.0 | 2014-01-15T20:43:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1160719.0 | 2014-01-16T07:19:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161659.0 | 2014-01-16T16:59:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161230.0 | 2014-01-16T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1162043.0 | 2014-01-16T20:43:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171659.0 | 2014-01-17T16:59:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1170719.0 | 2014-01-17T07:19:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1172043.0 | 2014-01-17T20:43:00.000+0000 | 54.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1181230.0 | 2014-01-18T12:30:00.000+0000 | 20.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1180719.0 | 2014-01-18T07:19:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191654.0 | 2014-01-19T16:54:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191230.0 | 2014-01-19T12:30:00.000+0000 | 29.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1190709.0 | 2014-01-19T07:09:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1192043.0 | 2014-01-19T20:43:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201659.0 | 2014-01-20T16:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1200719.0 | 2014-01-20T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1202043.0 | 2014-01-20T20:43:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211230.0 | 2014-01-21T12:30:00.000+0000 | 102.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1210719.0 | 2014-01-21T07:19:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211730.0 | 2014-01-21T17:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1212043.0 | 2014-01-21T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1220719.0 | 2014-01-22T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221659.0 | 2014-01-22T16:59:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221230.0 | 2014-01-22T12:30:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1222043.0 | 2014-01-22T20:43:00.000+0000 | 70.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1230719.0 | 2014-01-23T07:19:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231659.0 | 2014-01-23T16:59:00.000+0000 | 94.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231230.0 | 2014-01-23T12:30:00.000+0000 | 111.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1232043.0 | 2014-01-23T20:43:00.000+0000 | 13.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1240719.0 | 2014-01-24T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241659.0 | 2014-01-24T16:59:00.000+0000 | 84.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241230.0 | 2014-01-24T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1242043.0 | 2014-01-24T20:43:00.000+0000 | 56.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1251230.0 | 2014-01-25T12:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1250719.0 | 2014-01-25T07:19:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261654.0 | 2014-01-26T16:54:00.000+0000 | 113.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261230.0 | 2014-01-26T12:30:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1260709.0 | 2014-01-26T07:09:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1262043.0 | 2014-01-26T20:43:00.000+0000 | 31.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1271659.0 | 2014-01-27T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1270719.0 | 2014-01-27T07:19:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281230.0 | 2014-01-28T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1280719.0 | 2014-01-28T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281730.0 | 2014-01-28T17:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1282043.0 | 2014-01-28T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291659.0 | 2014-01-29T16:59:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1290719.0 | 2014-01-29T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1292043.0 | 2014-01-29T20:43:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1300719.0 | 2014-01-30T07:19:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301659.0 | 2014-01-30T16:59:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301230.0 | 2014-01-30T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1302043.0 | 2014-01-30T20:43:00.000+0000 | 93.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1310719.0 | 2014-01-31T07:19:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311659.0 | 2014-01-31T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311230.0 | 2014-01-31T12:30:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1312043.0 | 2014-01-31T20:43:00.000+0000 | 28.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171230.0 | 2014-01-17T12:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201250.0 | 2014-01-20T12:50:00.000+0000 | -12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291230.0 | 2014-01-29T12:30:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1011815.0 | 2014-01-01T18:15:00.000+0000 | 125.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1021815.0 | 2014-01-02T18:15:00.000+0000 | 33.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1031815.0 | 2014-01-03T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1040755.0 | 2014-01-04T07:55:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1051815.0 | 2014-01-05T18:15:00.000+0000 | 172.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1061815.0 | 2014-01-06T18:15:00.000+0000 | 151.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071815.0 | 2014-01-07T18:15:00.000+0000 | 43.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081815.0 | 2014-01-08T18:15:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091815.0 | 2014-01-09T18:15:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101815.0 | 2014-01-10T18:15:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1110730.0 | 2014-01-11T07:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121815.0 | 2014-01-12T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131815.0 | 2014-01-13T18:15:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141815.0 | 2014-01-14T18:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151815.0 | 2014-01-15T18:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161815.0 | 2014-01-16T18:15:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171815.0 | 2014-01-17T18:15:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1180730.0 | 2014-01-18T07:30:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191815.0 | 2014-01-19T18:15:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201815.0 | 2014-01-20T18:15:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211815.0 | 2014-01-21T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221815.0 | 2014-01-22T18:15:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231815.0 | 2014-01-23T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241815.0 | 2014-01-24T18:15:00.000+0000 | 84.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1250730.0 | 2014-01-25T07:30:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261815.0 | 2014-01-26T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1271815.0 | 2014-01-27T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281815.0 | 2014-01-28T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291815.0 | 2014-01-29T18:15:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301815.0 | 2014-01-30T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311815.0 | 2014-01-31T18:15:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2011055.0 | 2014-02-01T10:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2021025.0 | 2014-02-02T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2031025.0 | 2014-02-03T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2031750.0 | 2014-02-03T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2041025.0 | 2014-02-04T10:25:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2041750.0 | 2014-02-04T17:50:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2051025.0 | 2014-02-05T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2051750.0 | 2014-02-05T17:50:00.000+0000 | 29.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2061025.0 | 2014-02-06T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2061750.0 | 2014-02-06T17:50:00.000+0000 | 48.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2071025.0 | 2014-02-07T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2071750.0 | 2014-02-07T17:50:00.000+0000 | 20.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2081055.0 | 2014-02-08T10:55:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2091025.0 | 2014-02-09T10:25:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2091750.0 | 2014-02-09T17:50:00.000+0000 | 33.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2101025.0 | 2014-02-10T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2101750.0 | 2014-02-10T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2111025.0 | 2014-02-11T10:25:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2111750.0 | 2014-02-11T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2121025.0 | 2014-02-12T10:25:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2121750.0 | 2014-02-12T17:50:00.000+0000 | 158.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2131855.0 | 2014-02-13T18:55:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2131215.0 | 2014-02-13T12:15:00.000+0000 | 23.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2141855.0 | 2014-02-14T18:55:00.000+0000 | 22.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2141215.0 | 2014-02-14T12:15:00.000+0000 | 18.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2150935.0 | 2014-02-15T09:35:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2151635.0 | 2014-02-15T16:35:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2161855.0 | 2014-02-16T18:55:00.000+0000 | 58.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2161215.0 | 2014-02-16T12:15:00.000+0000 | 17.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2171855.0 | 2014-02-17T18:55:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2171215.0 | 2014-02-17T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2181855.0 | 2014-02-18T18:55:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2181215.0 | 2014-02-18T12:15:00.000+0000 | 58.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2191855.0 | 2014-02-19T18:55:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2191215.0 | 2014-02-19T12:15:00.000+0000 | 32.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2201855.0 | 2014-02-20T18:55:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2201215.0 | 2014-02-20T12:15:00.000+0000 | 28.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2211855.0 | 2014-02-21T18:55:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2211215.0 | 2014-02-21T12:15:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2220935.0 | 2014-02-22T09:35:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2221635.0 | 2014-02-22T16:35:00.000+0000 | 133.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2231855.0 | 2014-02-23T18:55:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2231215.0 | 2014-02-23T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2241855.0 | 2014-02-24T18:55:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2241215.0 | 2014-02-24T12:15:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2251855.0 | 2014-02-25T18:55:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2251215.0 | 2014-02-25T12:15:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2261855.0 | 2014-02-26T18:55:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2261215.0 | 2014-02-26T12:15:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2271855.0 | 2014-02-27T18:55:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2271215.0 | 2014-02-27T12:15:00.000+0000 | 32.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2281855.0 | 2014-02-28T18:55:00.000+0000 | 61.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2281215.0 | 2014-02-28T12:15:00.000+0000 | 94.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3010935.0 | 2014-03-01T09:35:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3011635.0 | 2014-03-01T16:35:00.000+0000 | 39.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3021855.0 | 2014-03-02T18:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3021215.0 | 2014-03-02T12:15:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3031855.0 | 2014-03-03T18:55:00.000+0000 | 25.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3031215.0 | 2014-03-03T12:15:00.000+0000 | 8.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3041855.0 | 2014-03-04T18:55:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3041215.0 | 2014-03-04T12:15:00.000+0000 | 28.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3051855.0 | 2014-03-05T18:55:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3051215.0 | 2014-03-05T12:15:00.000+0000 | 27.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3061855.0 | 2014-03-06T18:55:00.000+0000 | 20.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3061215.0 | 2014-03-06T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3071855.0 | 2014-03-07T18:55:00.000+0000 | 36.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3071215.0 | 2014-03-07T12:15:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3081940.0 | 2014-03-08T19:40:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3080830.0 | 2014-03-08T08:30:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3091905.0 | 2014-03-09T19:05:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3090840.0 | 2014-03-09T08:40:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3101905.0 | 2014-03-10T19:05:00.000+0000 | 49.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3100840.0 | 2014-03-10T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3111905.0 | 2014-03-11T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3110840.0 | 2014-03-11T08:40:00.000+0000 | 84.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3121905.0 | 2014-03-12T19:05:00.000+0000 | 26.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3120840.0 | 2014-03-12T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3131905.0 | 2014-03-13T19:05:00.000+0000 | 37.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3130840.0 | 2014-03-13T08:40:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3141905.0 | 2014-03-14T19:05:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3140840.0 | 2014-03-14T08:40:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3150830.0 | 2014-03-15T08:30:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3151940.0 | 2014-03-15T19:40:00.000+0000 | 95.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3161905.0 | 2014-03-16T19:05:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3160840.0 | 2014-03-16T08:40:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3171905.0 | 2014-03-17T19:05:00.000+0000 | 13.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3170840.0 | 2014-03-17T08:40:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3181905.0 | 2014-03-18T19:05:00.000+0000 | 52.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3180840.0 | 2014-03-18T08:40:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3191905.0 | 2014-03-19T19:05:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3190840.0 | 2014-03-19T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3201905.0 | 2014-03-20T19:05:00.000+0000 | 36.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3200840.0 | 2014-03-20T08:40:00.000+0000 | 68.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3211905.0 | 2014-03-21T19:05:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3210840.0 | 2014-03-21T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3220830.0 | 2014-03-22T08:30:00.000+0000 | 12.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3221940.0 | 2014-03-22T19:40:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3231905.0 | 2014-03-23T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3230840.0 | 2014-03-23T08:40:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3241905.0 | 2014-03-24T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3240840.0 | 2014-03-24T08:40:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3251905.0 | 2014-03-25T19:05:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3250840.0 | 2014-03-25T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3261905.0 | 2014-03-26T19:05:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3260840.0 | 2014-03-26T08:40:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3271905.0 | 2014-03-27T19:05:00.000+0000 | 13.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3270840.0 | 2014-03-27T08:40:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3281905.0 | 2014-03-28T19:05:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3280840.0 | 2014-03-28T08:40:00.000+0000 | 33.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3290830.0 | 2014-03-29T08:30:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3291940.0 | 2014-03-29T19:40:00.000+0000 | 231.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3301905.0 | 2014-03-30T19:05:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3300840.0 | 2014-03-30T08:40:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3311905.0 | 2014-03-31T19:05:00.000+0000 | 19.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3310840.0 | 2014-03-31T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1010850.0 | 2014-01-01T08:50:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1011755.0 | 2014-01-01T17:55:00.000+0000 | 160.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1021805.0 | 2014-01-02T18:05:00.000+0000 | 138.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1020905.0 | 2014-01-02T09:05:00.000+0000 | 5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1031805.0 | 2014-01-03T18:05:00.000+0000 | 154.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1030905.0 | 2014-01-03T09:05:00.000+0000 | 179.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1041655.0 | 2014-01-04T16:55:00.000+0000 | 113.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1040900.0 | 2014-01-04T09:00:00.000+0000 | 56.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1050905.0 | 2014-01-05T09:05:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1051805.0 | 2014-01-05T18:05:00.000+0000 | 53.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1061755.0 | 2014-01-06T17:55:00.000+0000 | 61.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1060905.0 | 2014-01-06T09:05:00.000+0000 | 23.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1071025.0 | 2014-01-07T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1071750.0 | 2014-01-07T17:50:00.000+0000 | 302.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1081025.0 | 2014-01-08T10:25:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1081750.0 | 2014-01-08T17:50:00.000+0000 | 52.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1091025.0 | 2014-01-09T10:25:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1091750.0 | 2014-01-09T17:50:00.000+0000 | 8.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1101025.0 | 2014-01-10T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1101750.0 | 2014-01-10T17:50:00.000+0000 | 92.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1111055.0 | 2014-01-11T10:55:00.000+0000 | 31.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1121025.0 | 2014-01-12T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1121750.0 | 2014-01-12T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1131025.0 | 2014-01-13T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1131750.0 | 2014-01-13T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1141025.0 | 2014-01-14T10:25:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1141750.0 | 2014-01-14T17:50:00.000+0000 | 127.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1151025.0 | 2014-01-15T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1151750.0 | 2014-01-15T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1161025.0 | 2014-01-16T10:25:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1161750.0 | 2014-01-16T17:50:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1171025.0 | 2014-01-17T10:25:00.000+0000 | 12.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1171750.0 | 2014-01-17T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1181055.0 | 2014-01-18T10:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1191025.0 | 2014-01-19T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1191750.0 | 2014-01-19T17:50:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1201025.0 | 2014-01-20T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1201750.0 | 2014-01-20T17:50:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1211025.0 | 2014-01-21T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1211750.0 | 2014-01-21T17:50:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1221025.0 | 2014-01-22T10:25:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1221750.0 | 2014-01-22T17:50:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1231025.0 | 2014-01-23T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1231750.0 | 2014-01-23T17:50:00.000+0000 | 30.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1241025.0 | 2014-01-24T10:25:00.000+0000 | 43.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1241750.0 | 2014-01-24T17:50:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1251055.0 | 2014-01-25T10:55:00.000+0000 | 5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1261025.0 | 2014-01-26T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1261750.0 | 2014-01-26T17:50:00.000+0000 | 27.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1271025.0 | 2014-01-27T10:25:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1271750.0 | 2014-01-27T17:50:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1281025.0 | 2014-01-28T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1281750.0 | 2014-01-28T17:50:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1291025.0 | 2014-01-29T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1291750.0 | 2014-01-29T17:50:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1301025.0 | 2014-01-30T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1301750.0 | 2014-01-30T17:50:00.000+0000 | 35.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1311025.0 | 2014-01-31T10:25:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1311750.0 | 2014-01-31T17:50:00.000+0000 | 25.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2011530.0 | 2014-02-01T15:30:00.000+0000 | -3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2021105.0 | 2014-02-02T11:05:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2031105.0 | 2014-02-03T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2041105.0 | 2014-02-04T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2051105.0 | 2014-02-05T11:05:00.000+0000 | 6.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2061105.0 | 2014-02-06T11:05:00.000+0000 | 40.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2071105.0 | 2014-02-07T11:05:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2081530.0 | 2014-02-08T15:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2091105.0 | 2014-02-09T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2101105.0 | 2014-02-10T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2111105.0 | 2014-02-11T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2121105.0 | 2014-02-12T11:05:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2130800.0 | 2014-02-13T08:00:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2140830.0 | 2014-02-14T08:30:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2150750.0 | 2014-02-15T07:50:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2160930.0 | 2014-02-16T09:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2170830.0 | 2014-02-17T08:30:00.000+0000 | 10.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2180830.0 | 2014-02-18T08:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2190830.0 | 2014-02-19T08:30:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2200830.0 | 2014-02-20T08:30:00.000+0000 | 9.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2210830.0 | 2014-02-21T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2220750.0 | 2014-02-22T07:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2230930.0 | 2014-02-23T09:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2240830.0 | 2014-02-24T08:30:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2250830.0 | 2014-02-25T08:30:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2260830.0 | 2014-02-26T08:30:00.000+0000 | 251.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2270830.0 | 2014-02-27T08:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2280830.0 | 2014-02-28T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3010750.0 | 2014-03-01T07:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3020930.0 | 2014-03-02T09:30:00.000+0000 | 35.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3030830.0 | 2014-03-03T08:30:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3040830.0 | 2014-03-04T08:30:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3050830.0 | 2014-03-05T08:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3060830.0 | 2014-03-06T08:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3070830.0 | 2014-03-07T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3081610.0 | 2014-03-08T16:10:00.000+0000 | 93.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3090950.0 | 2014-03-09T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3100950.0 | 2014-03-10T09:50:00.000+0000 | 8.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3110950.0 | 2014-03-11T09:50:00.000+0000 | 36.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3120950.0 | 2014-03-12T09:50:00.000+0000 | 68.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3130950.0 | 2014-03-13T09:50:00.000+0000 | 13.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3140950.0 | 2014-03-14T09:50:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3151610.0 | 2014-03-15T16:10:00.000+0000 | 16.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3160950.0 | 2014-03-16T09:50:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3170950.0 | 2014-03-17T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3180950.0 | 2014-03-18T09:50:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3190950.0 | 2014-03-19T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3200950.0 | 2014-03-20T09:50:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3210950.0 | 2014-03-21T09:50:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3221610.0 | 2014-03-22T16:10:00.000+0000 | 38.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3230950.0 | 2014-03-23T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3240950.0 | 2014-03-24T09:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3250950.0 | 2014-03-25T09:50:00.000+0000 | 4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3260950.0 | 2014-03-26T09:50:00.000+0000 | 5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3270950.0 | 2014-03-27T09:50:00.000+0000 | 12.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3280950.0 | 2014-03-28T09:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3291610.0 | 2014-03-29T16:10:00.000+0000 | 12.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3300950.0 | 2014-03-30T09:50:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3310950.0 | 2014-03-31T09:50:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1011635.0 | 2014-01-01T16:35:00.000+0000 | -7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1021635.0 | 2014-01-02T16:35:00.000+0000 | 96.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1031635.0 | 2014-01-03T16:35:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1041205.0 | 2014-01-04T12:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1051635.0 | 2014-01-05T16:35:00.000+0000 | 60.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1061635.0 | 2014-01-06T16:35:00.000+0000 | -7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1071105.0 | 2014-01-07T11:05:00.000+0000 | 14.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1081105.0 | 2014-01-08T11:05:00.000+0000 | 4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1091105.0 | 2014-01-09T11:05:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1101105.0 | 2014-01-10T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1111530.0 | 2014-01-11T15:30:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1121105.0 | 2014-01-12T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1131105.0 | 2014-01-13T11:05:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1141105.0 | 2014-01-14T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1151105.0 | 2014-01-15T11:05:00.000+0000 | 9.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1161105.0 | 2014-01-16T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1171105.0 | 2014-01-17T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1181530.0 | 2014-01-18T15:30:00.000+0000 | 48.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1191105.0 | 2014-01-19T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1201105.0 | 2014-01-20T11:05:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1211105.0 | 2014-01-21T11:05:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1221105.0 | 2014-01-22T11:05:00.000+0000 | 19.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1231105.0 | 2014-01-23T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1241105.0 | 2014-01-24T11:05:00.000+0000 | 72.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1251530.0 | 2014-01-25T15:30:00.000+0000 | 5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1261105.0 | 2014-01-26T11:05:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1271105.0 | 2014-01-27T11:05:00.000+0000 | -6.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1281105.0 | 2014-01-28T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1291105.0 | 2014-01-29T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1301105.0 | 2014-01-30T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1311105.0 | 2014-01-31T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3011530.0 | 2014-03-01T15:30:00.000+0000 | 72.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3010850.0 | 2014-03-01T08:50:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3011245.0 | 2014-03-01T12:45:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021610.0 | 2014-03-02T16:10:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021350.0 | 2014-03-02T13:50:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021800.0 | 2014-03-02T18:00:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031610.0 | 2014-03-03T16:10:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3030810.0 | 2014-03-03T08:10:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031350.0 | 2014-03-03T13:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031800.0 | 2014-03-03T18:00:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041610.0 | 2014-03-04T16:10:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3040810.0 | 2014-03-04T08:10:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041350.0 | 2014-03-04T13:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041800.0 | 2014-03-04T18:00:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051610.0 | 2014-03-05T16:10:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3050810.0 | 2014-03-05T08:10:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051350.0 | 2014-03-05T13:50:00.000+0000 | 48.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051800.0 | 2014-03-05T18:00:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061610.0 | 2014-03-06T16:10:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3060810.0 | 2014-03-06T08:10:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061350.0 | 2014-03-06T13:50:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061800.0 | 2014-03-06T18:00:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071610.0 | 2014-03-07T16:10:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3070810.0 | 2014-03-07T08:10:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071350.0 | 2014-03-07T13:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071800.0 | 2014-03-07T18:00:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3081540.0 | 2014-03-08T15:40:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3081335.0 | 2014-03-08T13:35:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3080945.0 | 2014-03-08T09:45:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091620.0 | 2014-03-09T16:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091820.0 | 2014-03-09T18:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091420.0 | 2014-03-09T14:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3100820.0 | 2014-03-10T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101420.0 | 2014-03-10T14:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101820.0 | 2014-03-10T18:20:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101620.0 | 2014-03-10T16:20:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3110820.0 | 2014-03-11T08:20:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111420.0 | 2014-03-11T14:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111820.0 | 2014-03-11T18:20:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111620.0 | 2014-03-11T16:20:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3120820.0 | 2014-03-12T08:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121420.0 | 2014-03-12T14:20:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121820.0 | 2014-03-12T18:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121620.0 | 2014-03-12T16:20:00.000+0000 | 24.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3130820.0 | 2014-03-13T08:20:00.000+0000 | 126.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131420.0 | 2014-03-13T14:20:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131820.0 | 2014-03-13T18:20:00.000+0000 | 55.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131620.0 | 2014-03-13T16:20:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3140820.0 | 2014-03-14T08:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141420.0 | 2014-03-14T14:20:00.000+0000 | 35.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141820.0 | 2014-03-14T18:20:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141620.0 | 2014-03-14T16:20:00.000+0000 | 36.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3151335.0 | 2014-03-15T13:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3150945.0 | 2014-03-15T09:45:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3151540.0 | 2014-03-15T15:40:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161620.0 | 2014-03-16T16:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161820.0 | 2014-03-16T18:20:00.000+0000 | 39.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161420.0 | 2014-03-16T14:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3170820.0 | 2014-03-17T08:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171420.0 | 2014-03-17T14:20:00.000+0000 | 112.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171820.0 | 2014-03-17T18:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171620.0 | 2014-03-17T16:20:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3180820.0 | 2014-03-18T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181420.0 | 2014-03-18T14:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181820.0 | 2014-03-18T18:20:00.000+0000 | 36.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181620.0 | 2014-03-18T16:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3190820.0 | 2014-03-19T08:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191420.0 | 2014-03-19T14:20:00.000+0000 | 54.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191820.0 | 2014-03-19T18:20:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191620.0 | 2014-03-19T16:20:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3200820.0 | 2014-03-20T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201420.0 | 2014-03-20T14:20:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201820.0 | 2014-03-20T18:20:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201620.0 | 2014-03-20T16:20:00.000+0000 | 79.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3210820.0 | 2014-03-21T08:20:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211420.0 | 2014-03-21T14:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211820.0 | 2014-03-21T18:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211620.0 | 2014-03-21T16:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3221335.0 | 2014-03-22T13:35:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3220945.0 | 2014-03-22T09:45:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3221540.0 | 2014-03-22T15:40:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231620.0 | 2014-03-23T16:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231820.0 | 2014-03-23T18:20:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231420.0 | 2014-03-23T14:20:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3240820.0 | 2014-03-24T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241420.0 | 2014-03-24T14:20:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241820.0 | 2014-03-24T18:20:00.000+0000 | 44.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241620.0 | 2014-03-24T16:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3250820.0 | 2014-03-25T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251420.0 | 2014-03-25T14:20:00.000+0000 | 70.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251820.0 | 2014-03-25T18:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251620.0 | 2014-03-25T16:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3260820.0 | 2014-03-26T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261420.0 | 2014-03-26T14:20:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261820.0 | 2014-03-26T18:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261620.0 | 2014-03-26T16:20:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3270820.0 | 2014-03-27T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271420.0 | 2014-03-27T14:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271820.0 | 2014-03-27T18:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271620.0 | 2014-03-27T16:20:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3280820.0 | 2014-03-28T08:20:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281420.0 | 2014-03-28T14:20:00.000+0000 | 52.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281820.0 | 2014-03-28T18:20:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281620.0 | 2014-03-28T16:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3291335.0 | 2014-03-29T13:35:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3290945.0 | 2014-03-29T09:45:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3291540.0 | 2014-03-29T15:40:00.000+0000 | 35.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301620.0 | 2014-03-30T16:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301820.0 | 2014-03-30T18:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301420.0 | 2014-03-30T14:20:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3310820.0 | 2014-03-31T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311420.0 | 2014-03-31T14:20:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311820.0 | 2014-03-31T18:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311620.0 | 2014-03-31T16:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1010800.0 | 2014-01-01T08:00:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1011210.0 | 2014-01-01T12:10:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1011835.0 | 2014-01-01T18:35:00.000+0000 | 53.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1021215.0 | 2014-01-02T12:15:00.000+0000 | 43.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1021835.0 | 2014-01-02T18:35:00.000+0000 | 200.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1020800.0 | 2014-01-02T08:00:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1031215.0 | 2014-01-03T12:15:00.000+0000 | 185.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1031835.0 | 2014-01-03T18:35:00.000+0000 | 226.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1030800.0 | 2014-01-03T08:00:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1041420.0 | 2014-01-04T14:20:00.000+0000 | 174.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1040835.0 | 2014-01-04T08:35:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1051215.0 | 2014-01-05T12:15:00.000+0000 | 85.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1051835.0 | 2014-01-05T18:35:00.000+0000 | 283.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1050800.0 | 2014-01-05T08:00:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1061215.0 | 2014-01-06T12:15:00.000+0000 | 150.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1061835.0 | 2014-01-06T18:35:00.000+0000 | 133.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1060800.0 | 2014-01-06T08:00:00.000+0000 | 102.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071240.0 | 2014-01-07T12:40:00.000+0000 | 57.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071455.0 | 2014-01-07T14:55:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1070905.0 | 2014-01-07T09:05:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071735.0 | 2014-01-07T17:35:00.000+0000 | 131.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081240.0 | 2014-01-08T12:40:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081455.0 | 2014-01-08T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1080905.0 | 2014-01-08T09:05:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081735.0 | 2014-01-08T17:35:00.000+0000 | 34.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091240.0 | 2014-01-09T12:40:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091455.0 | 2014-01-09T14:55:00.000+0000 | 24.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1090905.0 | 2014-01-09T09:05:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091735.0 | 2014-01-09T17:35:00.000+0000 | 28.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101240.0 | 2014-01-10T12:40:00.000+0000 | 50.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101455.0 | 2014-01-10T14:55:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1100905.0 | 2014-01-10T09:05:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101735.0 | 2014-01-10T17:35:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1111305.0 | 2014-01-11T13:05:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1111805.0 | 2014-01-11T18:05:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1110950.0 | 2014-01-11T09:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121235.0 | 2014-01-12T12:35:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121455.0 | 2014-01-12T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121735.0 | 2014-01-12T17:35:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131240.0 | 2014-01-13T12:40:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131455.0 | 2014-01-13T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1130850.0 | 2014-01-13T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131735.0 | 2014-01-13T17:35:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141240.0 | 2014-01-14T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141455.0 | 2014-01-14T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1140850.0 | 2014-01-14T08:50:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141735.0 | 2014-01-14T17:35:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151240.0 | 2014-01-15T12:40:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151455.0 | 2014-01-15T14:55:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1150850.0 | 2014-01-15T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151735.0 | 2014-01-15T17:35:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161240.0 | 2014-01-16T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161455.0 | 2014-01-16T14:55:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1160850.0 | 2014-01-16T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161735.0 | 2014-01-16T17:35:00.000+0000 | 52.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171240.0 | 2014-01-17T12:40:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171455.0 | 2014-01-17T14:55:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1170850.0 | 2014-01-17T08:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171735.0 | 2014-01-17T17:35:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1181305.0 | 2014-01-18T13:05:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1181805.0 | 2014-01-18T18:05:00.000+0000 | 42.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1180950.0 | 2014-01-18T09:50:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191235.0 | 2014-01-19T12:35:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191455.0 | 2014-01-19T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191735.0 | 2014-01-19T17:35:00.000+0000 | 64.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201240.0 | 2014-01-20T12:40:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201455.0 | 2014-01-20T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1200850.0 | 2014-01-20T08:50:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201735.0 | 2014-01-20T17:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211240.0 | 2014-01-21T12:40:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211455.0 | 2014-01-21T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1210850.0 | 2014-01-21T08:50:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211735.0 | 2014-01-21T17:35:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221240.0 | 2014-01-22T12:40:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221455.0 | 2014-01-22T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1220850.0 | 2014-01-22T08:50:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221735.0 | 2014-01-22T17:35:00.000+0000 | 118.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231240.0 | 2014-01-23T12:40:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231455.0 | 2014-01-23T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1230850.0 | 2014-01-23T08:50:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231735.0 | 2014-01-23T17:35:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241240.0 | 2014-01-24T12:40:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241455.0 | 2014-01-24T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1240850.0 | 2014-01-24T08:50:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241735.0 | 2014-01-24T17:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1251305.0 | 2014-01-25T13:05:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1251805.0 | 2014-01-25T18:05:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1250950.0 | 2014-01-25T09:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261235.0 | 2014-01-26T12:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261455.0 | 2014-01-26T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261735.0 | 2014-01-26T17:35:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271240.0 | 2014-01-27T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271455.0 | 2014-01-27T14:55:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1270850.0 | 2014-01-27T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271735.0 | 2014-01-27T17:35:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281240.0 | 2014-01-28T12:40:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281455.0 | 2014-01-28T14:55:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1280850.0 | 2014-01-28T08:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281735.0 | 2014-01-28T17:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291240.0 | 2014-01-29T12:40:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291455.0 | 2014-01-29T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1290850.0 | 2014-01-29T08:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291735.0 | 2014-01-29T17:35:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301240.0 | 2014-01-30T12:40:00.000+0000 | 38.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301455.0 | 2014-01-30T14:55:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1300850.0 | 2014-01-30T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301735.0 | 2014-01-30T17:35:00.000+0000 | 57.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311240.0 | 2014-01-31T12:40:00.000+0000 | 31.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311455.0 | 2014-01-31T14:55:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1310850.0 | 2014-01-31T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311735.0 | 2014-01-31T17:35:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2011305.0 | 2014-02-01T13:05:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2011805.0 | 2014-02-01T18:05:00.000+0000 | -6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2010950.0 | 2014-02-01T09:50:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021455.0 | 2014-02-02T14:55:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021235.0 | 2014-02-02T12:35:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021650.0 | 2014-02-02T16:50:00.000+0000 | 77.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031240.0 | 2014-02-03T12:40:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031455.0 | 2014-02-03T14:55:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2030850.0 | 2014-02-03T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031735.0 | 2014-02-03T17:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041240.0 | 2014-02-04T12:40:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041455.0 | 2014-02-04T14:55:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2040850.0 | 2014-02-04T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041735.0 | 2014-02-04T17:35:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051240.0 | 2014-02-05T12:40:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051455.0 | 2014-02-05T14:55:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2050850.0 | 2014-02-05T08:50:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051735.0 | 2014-02-05T17:35:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061240.0 | 2014-02-06T12:40:00.000+0000 | 43.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061455.0 | 2014-02-06T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2060850.0 | 2014-02-06T08:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061735.0 | 2014-02-06T17:35:00.000+0000 | 34.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071240.0 | 2014-02-07T12:40:00.000+0000 | 88.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071455.0 | 2014-02-07T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2070850.0 | 2014-02-07T08:50:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071735.0 | 2014-02-07T17:35:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2081305.0 | 2014-02-08T13:05:00.000+0000 | -6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2081805.0 | 2014-02-08T18:05:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2080950.0 | 2014-02-08T09:50:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091235.0 | 2014-02-09T12:35:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091455.0 | 2014-02-09T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091735.0 | 2014-02-09T17:35:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101240.0 | 2014-02-10T12:40:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101455.0 | 2014-02-10T14:55:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2100850.0 | 2014-02-10T08:50:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101735.0 | 2014-02-10T17:35:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111240.0 | 2014-02-11T12:40:00.000+0000 | 145.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111455.0 | 2014-02-11T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2110850.0 | 2014-02-11T08:50:00.000+0000 | 96.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111735.0 | 2014-02-11T17:35:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121240.0 | 2014-02-12T12:40:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121455.0 | 2014-02-12T14:55:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2120850.0 | 2014-02-12T08:50:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121735.0 | 2014-02-12T17:35:00.000+0000 | 39.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131350.0 | 2014-02-13T13:50:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131610.0 | 2014-02-13T16:10:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2130810.0 | 2014-02-13T08:10:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131800.0 | 2014-02-13T18:00:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141610.0 | 2014-02-14T16:10:00.000+0000 | 37.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2140810.0 | 2014-02-14T08:10:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141350.0 | 2014-02-14T13:50:00.000+0000 | 46.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141800.0 | 2014-02-14T18:00:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
Building the Graph
Now that we've imported our data, we're going to need to build our graph. To do so we're going to do two things. We are going to build the structure of the vertices (or nodes) and we're going to build the structure of the edges. What's awesome about GraphFrames is that this process is incredibly simple.
- Rename IATA airport code to id in the Vertices Table
- Start and End airports to src and dst for the Edges Table (flights)
These are required naming conventions for vertices and edges in GraphFrames.
WARNING: If the graphframes package, required in the cell below, is not installed, follow the instructions here.
// Note, ensure you have already installed the GraphFrames spack-package
import org.apache.spark.sql.functions._
import org.graphframes._
// Create Vertices (airports) and Edges (flights)
val tripVertices = airports.withColumnRenamed("IATA", "id").distinct()
val tripEdges = departureDelays_geo.select("tripid", "delay", "src", "dst", "city_dst", "state_dst")
// Cache Vertices and Edges
tripEdges.cache()
tripVertices.cache()
import org.apache.spark.sql.functions._
import org.graphframes._
tripVertices: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, City: string ... 2 more fields]
tripEdges: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 4 more fields]
res5: tripVertices.type = [id: string, City: string ... 2 more fields]
// Vertices
// The vertices of our graph are the airports
display(tripVertices)
| id | City | State | Country |
|---|---|---|---|
| FAT | Fresno | CA | USA |
| CMH | Columbus | OH | USA |
| PHX | Phoenix | AZ | USA |
| PAH | Paducah | KY | USA |
| COS | Colorado Springs | CO | USA |
| RNO | Reno | NV | USA |
| MYR | Myrtle Beach | SC | USA |
| VLD | Valdosta | GA | USA |
| BPT | Beaumont | TX | USA |
| CAE | Columbia | SC | USA |
| PSC | Pasco | WA | USA |
| SRQ | Sarasota | FL | USA |
| LAX | Los Angeles | CA | USA |
| DAY | Dayton | OH | USA |
| AVP | Wilkes-Barre | PA | USA |
| MFR | Medford | OR | USA |
| JFK | New York | NY | USA |
| BNA | Nashville | TN | USA |
| CLT | Charlotte | NC | USA |
| LAS | Las Vegas | NV | USA |
| BDL | Hartford | CT | USA |
| ILG | Wilmington | DE | USA |
| ACT | Waco | TX | USA |
| ATW | Appleton | WI | USA |
| RHI | Rhinelander | WI | USA |
| PWM | Portland | ME | USA |
| SJT | San Angelo | TX | USA |
| APN | Alpena | MI | USA |
| GRB | Green Bay | WI | USA |
| CAK | Akron | OH | USA |
| LAN | Lansing | MI | USA |
| FLL | Fort Lauderdale | FL | USA |
| MSY | New Orleans | LA | USA |
| SAT | San Antonio | TX | USA |
| TPA | Tampa | FL | USA |
| COD | Cody | WY | USA |
| MOD | Modesto | CA | USA |
| GTR | Columbus | MS | USA |
| BTV | Burlington | VT | USA |
| RDD | Redding | CA | USA |
| HLN | Helena | MT | USA |
| CPR | Casper | WY | USA |
| FWA | Fort Wayne | IN | USA |
| IMT | Iron Mountain | MI | USA |
| DTW | Detroit | MI | USA |
| BZN | Bozeman | MT | USA |
| SBN | South Bend | IN | USA |
| SPS | Wichita Falls | TX | USA |
| BFL | Bakersfield | CA | USA |
| HOB | Hobbs | NM | USA |
| TVC | Traverse City | MI | USA |
| CLE | Cleveland | OH | USA |
| ABR | Aberdeen | SD | USA |
| CHS | Charleston | SC | USA |
| GUC | Gunnison | CO | USA |
| IND | Indianapolis | IN | USA |
| SDF | Louisville | KY | USA |
| SAN | San Diego | CA | USA |
| RSW | Fort Myers | FL | USA |
| BOS | Boston | MA | USA |
| TUL | Tulsa | OK | USA |
| AGS | Augusta | GA | USA |
| MOB | Mobile | AL | USA |
| TUS | Tucson | AZ | USA |
| KTN | Ketchikan | AK | USA |
| BTR | Baton Rouge | LA | USA |
| PNS | Pensacola | FL | USA |
| ABQ | Albuquerque | NM | USA |
| LGA | New York | NY | USA |
| DAL | Dallas | TX | USA |
| JNU | Juneau | AK | USA |
| MAF | Midland | TX | USA |
| RIC | Richmond | VA | USA |
| FAR | Fargo | ND | USA |
| MTJ | Montrose | CO | USA |
| AMA | Amarillo | TX | USA |
| ROC | Rochester | NY | USA |
| SHV | Shreveport | LA | USA |
| YAK | Yakutat | AK | USA |
| CSG | Columbus | GA | USA |
| ALO | Waterloo | IA | USA |
| DRO | Durango | CO | USA |
| CRP | Corpus Christi | TX | USA |
| FNT | Flint | MI | USA |
| GSO | Greensboro | NC | USA |
| LWS | Lewiston | ID | USA |
| TOL | Toledo | OH | USA |
| GTF | Great Falls | MT | USA |
| MKE | Milwaukee | WI | USA |
| RKS | Rock Springs | WY | USA |
| STL | St. Louis | MO | USA |
| MHT | Manchester | NH | USA |
| CRW | Charleston | WV | USA |
| SLC | Salt Lake City | UT | USA |
| ACV | Eureka | CA | USA |
| DFW | Dallas | TX | USA |
| OME | Nome | AK | USA |
| ORF | Norfolk | VA | USA |
| RDU | Raleigh | NC | USA |
| SYR | Syracuse | NY | USA |
| BQK | Brunswick | GA | USA |
| ROA | Roanoke | VA | USA |
| OKC | Oklahoma City | OK | USA |
| EYW | Key West | FL | USA |
| BOI | Boise | ID | USA |
| ABY | Albany | GA | USA |
| PIA | Peoria | IL | USA |
| GRI | Grand Island | NE | USA |
| PBI | West Palm Beach | FL | USA |
| FSM | Fort Smith | AR | USA |
| TYS | Knoxville | TN | USA |
| DAB | Daytona Beach | FL | USA |
| TTN | Trenton | NJ | USA |
| MDT | Harrisburg | PA | USA |
| AUS | Austin | TX | USA |
| DCA | Washington DC | null | USA |
| PIH | Pocatello | ID | USA |
| GCC | Gillette | WY | USA |
| BUR | Burbank | CA | USA |
| GRK | Killeen | TX | USA |
| LBB | Lubbock | TX | USA |
| JAN | Jackson | MS | USA |
| MSP | Minneapolis | MN | USA |
| SAF | Santa Fe | NM | USA |
| HPN | White Plains | NY | USA |
| DHN | Dothan | AL | USA |
| PSP | Palm Springs | CA | USA |
| INL | International Falls | MN | USA |
| CIC | Chico | CA | USA |
| EGE | Vail | CO | USA |
| CLD | Carlsbad | CA | USA |
| RST | Rochester | MN | USA |
| DEN | Denver | CO | USA |
| EUG | Eugene | OR | USA |
| LFT | Lafayette | LA | USA |
| PVD | Providence | RI | USA |
| DBQ | Dubuque | IA | USA |
| SAV | Savannah | GA | USA |
| SFO | San Francisco | CA | USA |
| JAX | Jacksonville | FL | USA |
| LIT | Little Rock | AR | USA |
| IDA | Idaho Falls | ID | USA |
| TYR | Tyler | TX | USA |
| ANC | Anchorage | AK | USA |
| HRL | Harlingen | TX | USA |
| LMT | Klamath Falls | OR | USA |
| PHF | Newport News | VA | USA |
| HOU | Houston | TX | USA |
| SPI | Springfield | IL | USA |
| MOT | Minot | ND | USA |
| BTM | Butte | MT | USA |
| SMF | Sacramento | CA | USA |
| HIB | Hibbing | MN | USA |
| ROW | Roswell | NM | USA |
| MBS | Saginaw | MI | USA |
| ILM | Wilmington | NC | USA |
| LGB | Long Beach | CA | USA |
| IAD | Washington DC | null | USA |
| ICT | Wichita | KS | USA |
| BGR | Bangor | ME | USA |
| ELP | El Paso | TX | USA |
| SUX | Sioux City | IA | USA |
| HSV | Huntsville | AL | USA |
| SIT | Sitka | AK | USA |
| CWA | Wausau | WI | USA |
| LCH | Lake Charles | LA | USA |
| CMI | Champaign | IL | USA |
| ELM | Elmira | NY | USA |
| CLL | College Station | TX | USA |
| VPS | Fort Walton Beach | FL | USA |
| MSN | Madison | WI | USA |
| MHK | Manhattan | KS | USA |
| OAJ | Jacksonville | NC | USA |
| EKO | Elko | NV | USA |
| FSD | Sioux Falls | SD | USA |
| EWR | Newark | NJ | USA |
| MEM | Memphis | TN | USA |
| ADQ | Kodiak | AK | USA |
| GGG | Longview | TX | USA |
| BLI | Bellingham | WA | USA |
| OMA | Omaha | NE | USA |
| ATL | Atlanta | GA | USA |
| SJC | San Jose | CA | USA |
| SBA | Santa Barbara | CA | USA |
| ONT | Ontario | CA | USA |
| EAU | Eau Claire | WI | USA |
| GPT | Gulfport | MS | USA |
| SUN | Sun Valley | ID | USA |
| CHO | Charlottesville | VA | USA |
| MSO | Missoula | MT | USA |
| CMX | Hancock | MI | USA |
| ORD | Chicago | IL | USA |
| EVV | Evansville | IN | USA |
| MLU | Monroe | LA | USA |
| LAW | Lawton | OK | USA |
| BRW | Barrow | AK | USA |
| SBP | San Luis Obispo | CA | USA |
| LIH | Lihue, Kauai | HI | USA |
| GJT | Grand Junction | CO | USA |
| FAI | Fairbanks | AK | USA |
| BIS | Bismarck | ND | USA |
| SNA | Orange County | CA | USA |
| LAR | Laramie | WY | USA |
| JLN | Joplin | MO | USA |
| LRD | Laredo | TX | USA |
| LEX | Lexington | KY | USA |
| SGU | St. George | UT | USA |
| MCI | Kansas City | MO | USA |
| AEX | Alexandria | LA | USA |
| ISN | Williston | ND | USA |
| TXK | Texarkana | AR | USA |
| BIL | Billings | MT | USA |
| CID | Cedar Rapids | IA | USA |
| PDX | Portland | OR | USA |
| ABE | Allentown | PA | USA |
| DIK | Dickinson | ND | USA |
| ART | Watertown | NY | USA |
| GCK | Garden City | KS | USA |
| BET | Bethel | AK | USA |
| AVL | Asheville | NC | USA |
| MCO | Orlando | FL | USA |
| GSP | Greenville | SC | USA |
| TWF | Twin Falls | ID | USA |
| MKG | Muskegon | MI | USA |
| FAY | Fayetteville | NC | USA |
| SCE | State College | PA | USA |
| EWN | New Bern | NC | USA |
| XNA | Fayetteville | AR | USA |
| MRY | Monterey | CA | USA |
| MLB | Melbourne | FL | USA |
| HNL | Honolulu, Oahu | HI | USA |
| CVG | Cincinnati | OH | USA |
| RAP | Rapid City | SD | USA |
| AZO | Kalamazoo | MI | USA |
| WRG | Wrangell | AK | USA |
| ISP | Islip | NY | USA |
| FLG | Flagstaff | AZ | USA |
| BHM | Birmingham | AL | USA |
| ALB | Albany | NY | USA |
| SEA | Seattle | WA | USA |
| GRR | Grand Rapids | MI | USA |
| CHA | Chattanooga | TN | USA |
| IAH | Houston | TX | USA |
| SMX | Santa Maria | CA | USA |
| MDW | Chicago | IL | USA |
| MQT | Marquette | MI | USA |
| LNK | Lincoln | NE | USA |
| RDM | Redmond | OR | USA |
| DLH | Duluth | MN | USA |
| DSM | Des Moines | IA | USA |
| OAK | Oakland | CA | USA |
| PHL | Philadelphia | PA | USA |
| FCA | Kalispell | MT | USA |
| MFE | McAllen | TX | USA |
| OGG | Kahului, Maui | HI | USA |
| YUM | Yuma | AZ | USA |
| BMI | Bloomington | IL | USA |
| GEG | Spokane | WA | USA |
| TLH | Tallahassee | FL | USA |
| LSE | La Crosse | WI | USA |
| MIA | Miami | FL | USA |
| BRO | Brownsville | TX | USA |
| JAC | Jackson Hole | WY | USA |
| CDV | Cordova | AK | USA |
| TRI | Tri-City Airport | TN | USA |
| SWF | Newburgh | NY | USA |
| MGM | Montgomery | AL | USA |
| BWI | Baltimore | MD | USA |
| SGF | Springfield | MO | USA |
| GFK | Grand Forks | ND | USA |
| GNV | Gainesville | FL | USA |
| OTH | North Bend | OR | USA |
| PIT | Pittsburgh | PA | USA |
| BJI | Bemidji | MN | USA |
| ASE | Aspen | CO | USA |
| BUF | Buffalo | NY | USA |
| COU | Columbia | MO | USA |
| ABI | Abilene | TX | USA |
| MLI | Moline | IL | USA |
// Edges
// The edges of our graph are the flights between airports
display(tripEdges)
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 1011111.0 | -5.0 | MSP | INL | International Falls | MN |
| 1021111.0 | 7.0 | MSP | INL | International Falls | MN |
| 1031111.0 | 0.0 | MSP | INL | International Falls | MN |
| 1041925.0 | 0.0 | MSP | INL | International Falls | MN |
| 1061115.0 | 33.0 | MSP | INL | International Falls | MN |
| 1071115.0 | 23.0 | MSP | INL | International Falls | MN |
| 1081115.0 | -9.0 | MSP | INL | International Falls | MN |
| 1091115.0 | 11.0 | MSP | INL | International Falls | MN |
| 1101115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1112015.0 | -7.0 | MSP | INL | International Falls | MN |
| 1121925.0 | -5.0 | MSP | INL | International Falls | MN |
| 1131115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1141115.0 | -6.0 | MSP | INL | International Falls | MN |
| 1151115.0 | -7.0 | MSP | INL | International Falls | MN |
| 1161115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1171115.0 | 4.0 | MSP | INL | International Falls | MN |
| 1182015.0 | -5.0 | MSP | INL | International Falls | MN |
| 1191925.0 | -7.0 | MSP | INL | International Falls | MN |
| 1201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 1211115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1221115.0 | -4.0 | MSP | INL | International Falls | MN |
| 1231115.0 | -4.0 | MSP | INL | International Falls | MN |
| 1241115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1252015.0 | -12.0 | MSP | INL | International Falls | MN |
| 1261925.0 | -5.0 | MSP | INL | International Falls | MN |
| 1271115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1281115.0 | -8.0 | MSP | INL | International Falls | MN |
| 1291115.0 | -2.0 | MSP | INL | International Falls | MN |
| 1301115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1311115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2012015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2022015.0 | 0.0 | MSP | INL | International Falls | MN |
| 2031115.0 | -7.0 | MSP | INL | International Falls | MN |
| 2041115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2051115.0 | -4.0 | MSP | INL | International Falls | MN |
| 2061115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2071115.0 | -15.0 | MSP | INL | International Falls | MN |
| 2082015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2091925.0 | 1.0 | MSP | INL | International Falls | MN |
| 2101115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2111115.0 | -7.0 | MSP | INL | International Falls | MN |
| 2121115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2131115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2141115.0 | -11.0 | MSP | INL | International Falls | MN |
| 2152015.0 | 16.0 | MSP | INL | International Falls | MN |
| 2161925.0 | 169.0 | MSP | INL | International Falls | MN |
| 2171115.0 | 27.0 | MSP | INL | International Falls | MN |
| 2181115.0 | 96.0 | MSP | INL | International Falls | MN |
| 2191115.0 | -9.0 | MSP | INL | International Falls | MN |
| 2201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2211115.0 | -4.0 | MSP | INL | International Falls | MN |
| 2222015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2231925.0 | -3.0 | MSP | INL | International Falls | MN |
| 2241115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2251115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2261115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2271115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2281115.0 | 5.0 | MSP | INL | International Falls | MN |
| 3012015.0 | -4.0 | MSP | INL | International Falls | MN |
| 3022000.0 | 0.0 | MSP | INL | International Falls | MN |
| 3031115.0 | 17.0 | MSP | INL | International Falls | MN |
| 3041115.0 | 0.0 | MSP | INL | International Falls | MN |
| 3051115.0 | -7.0 | MSP | INL | International Falls | MN |
| 3061115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3071115.0 | -10.0 | MSP | INL | International Falls | MN |
| 3082000.0 | -11.0 | MSP | INL | International Falls | MN |
| 3092000.0 | -9.0 | MSP | INL | International Falls | MN |
| 3101115.0 | -10.0 | MSP | INL | International Falls | MN |
| 3111115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3121115.0 | -6.0 | MSP | INL | International Falls | MN |
| 3131115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3141115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3152000.0 | -11.0 | MSP | INL | International Falls | MN |
| 3162000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3171115.0 | 25.0 | MSP | INL | International Falls | MN |
| 3181115.0 | 2.0 | MSP | INL | International Falls | MN |
| 3191115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 3211115.0 | 0.0 | MSP | INL | International Falls | MN |
| 3222000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3232000.0 | -9.0 | MSP | INL | International Falls | MN |
| 3241115.0 | -9.0 | MSP | INL | International Falls | MN |
| 3251115.0 | -4.0 | MSP | INL | International Falls | MN |
| 3261115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3271115.0 | 9.0 | MSP | INL | International Falls | MN |
| 3281115.0 | -7.0 | MSP | INL | International Falls | MN |
| 3292000.0 | -19.0 | MSP | INL | International Falls | MN |
| 3302000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3311115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2011230.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2010719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2021230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2020709.0 | 59.0 | EWR | MSY | New Orleans | LA |
| 2021654.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2030719.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 2031659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2031230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2032043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2041230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 2040719.0 | 168.0 | EWR | MSY | New Orleans | LA |
| 2041730.0 | 88.0 | EWR | MSY | New Orleans | LA |
| 2042043.0 | 106.0 | EWR | MSY | New Orleans | LA |
| 2051659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2050719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2050929.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2052043.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2060719.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2061659.0 | 82.0 | EWR | MSY | New Orleans | LA |
| 2061230.0 | 61.0 | EWR | MSY | New Orleans | LA |
| 2062043.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2070719.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2071659.0 | 19.0 | EWR | MSY | New Orleans | LA |
| 2071230.0 | 27.0 | EWR | MSY | New Orleans | LA |
| 2072048.0 | 47.0 | EWR | MSY | New Orleans | LA |
| 2081230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2080719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2091229.0 | 95.0 | EWR | MSY | New Orleans | LA |
| 2091654.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2090709.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2092043.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2100719.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 2101659.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 2101230.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2102043.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2111230.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2110719.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2111730.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 2112043.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2120719.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 2121230.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2120929.0 | 89.0 | EWR | MSY | New Orleans | LA |
| 2122043.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2130738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2132041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2140729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2142041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2151206.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2150659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2160705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2170729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2172041.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2180738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2182041.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2190727.0 | 15.0 | EWR | MSY | New Orleans | LA |
| 2200738.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2202041.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 2210729.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2212041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2221206.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2220659.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2232041.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2230705.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2240729.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2242041.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2250738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2260727.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2262041.0 | 174.0 | EWR | MSY | New Orleans | LA |
| 2270738.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2272041.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2280729.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 2282041.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 2281000.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 2051230.0 | 216.0 | EWR | MSY | New Orleans | LA |
| 2131536.0 | 273.0 | EWR | MSY | New Orleans | LA |
| 2141536.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2151902.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 2151536.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 2162041.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2161536.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2171536.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 2181536.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 2191536.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 2201536.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2211536.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 2221900.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2221536.0 | 65.0 | EWR | MSY | New Orleans | LA |
| 2231536.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2241536.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2251536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2261536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2271536.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2281536.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2010730.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2021815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2031815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2041815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2051815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2061815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2071815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2080730.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2091815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2101815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2111815.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 2121815.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 2131805.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2141635.0 | 52.0 | EWR | MSY | New Orleans | LA |
| 2150730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2161635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2171635.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2181635.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2191635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2201635.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2211635.0 | 292.0 | EWR | MSY | New Orleans | LA |
| 2220730.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 2231635.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2241635.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2251635.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2261635.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2271635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2281635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3011206.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3010659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3022041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3020705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3030729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3032041.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 3040738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3050727.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3052041.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 3060705.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3061252.0 | 67.0 | EWR | MSY | New Orleans | LA |
| 3062100.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 3070705.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3071252.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3072100.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 3080705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3081250.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3091255.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 3092059.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3100705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3101252.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 3102059.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3112059.0 | 181.0 | EWR | MSY | New Orleans | LA |
| 3111252.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3122059.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3121252.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 3130705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3131252.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3132059.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3140705.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 3141252.0 | 39.0 | EWR | MSY | New Orleans | LA |
| 3142059.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3150700.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3151250.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 3161255.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3162059.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3170705.0 | -11.0 | EWR | MSY | New Orleans | LA |
| 3171252.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3172059.0 | 132.0 | EWR | MSY | New Orleans | LA |
| 3182059.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3181252.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3192059.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3191252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3200705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3201252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3202059.0 | 77.0 | EWR | MSY | New Orleans | LA |
| 3210705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3211252.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3212059.0 | 11.0 | EWR | MSY | New Orleans | LA |
| 3220705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3221250.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3221600.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3231255.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3240705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3241252.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3242059.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 3252059.0 | 121.0 | EWR | MSY | New Orleans | LA |
| 3251252.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3262059.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 3261252.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3270705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3271252.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3272059.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3280705.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3281252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3282059.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3291250.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3290705.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3291600.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3301255.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3302059.0 | 166.0 | EWR | MSY | New Orleans | LA |
| 3310705.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3311252.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 3312059.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 3011902.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3011536.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3021536.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3031536.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 3041536.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3051536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3090715.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3110705.0 | -11.0 | EWR | MSY | New Orleans | LA |
| 3120659.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3160715.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3180705.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3190659.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3230715.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3250705.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3260659.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 3300715.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3010730.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3021635.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3031635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3041635.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 3051635.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3061635.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3071635.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3080730.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3091825.0 | 45.0 | EWR | MSY | New Orleans | LA |
| 3101825.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3111825.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3121825.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3131825.0 | 123.0 | EWR | MSY | New Orleans | LA |
| 3141825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3150730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3161825.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 3171825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3181825.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3191825.0 | 223.0 | EWR | MSY | New Orleans | LA |
| 3201825.0 | 178.0 | EWR | MSY | New Orleans | LA |
| 3211825.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3220730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3231825.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3241825.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3251825.0 | 222.0 | EWR | MSY | New Orleans | LA |
| 3261825.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 3271825.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3281825.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 3290730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3301825.0 | 139.0 | EWR | MSY | New Orleans | LA |
| 3311825.0 | 25.0 | EWR | MSY | New Orleans | LA |
| 1020705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1030705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1040655.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1050703.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 1060705.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 1071230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 1070719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1071730.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 1072043.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1080719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1081659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1081230.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 1082043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1090719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1091659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1091230.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1092043.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 1100719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1101659.0 | 244.0 | EWR | MSY | New Orleans | LA |
| 1101230.0 | 110.0 | EWR | MSY | New Orleans | LA |
| 1102043.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1111230.0 | 87.0 | EWR | MSY | New Orleans | LA |
| 1110719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1121654.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1121230.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 1120709.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1122043.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1130719.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 1131659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1131230.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1132043.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 1141230.0 | 30.0 | EWR | MSY | New Orleans | LA |
| 1140719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1141730.0 | 69.0 | EWR | MSY | New Orleans | LA |
| 1142043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1150719.0 | 42.0 | EWR | MSY | New Orleans | LA |
| 1151659.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1151230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1152043.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1160719.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1161659.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 1161230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 1162043.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1171659.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 1170719.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 1172043.0 | 54.0 | EWR | MSY | New Orleans | LA |
| 1181230.0 | 20.0 | EWR | MSY | New Orleans | LA |
| 1180719.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1191654.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1191230.0 | 29.0 | EWR | MSY | New Orleans | LA |
| 1190709.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1192043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1200719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1202043.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1211230.0 | 102.0 | EWR | MSY | New Orleans | LA |
| 1210719.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1211730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1212043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1220719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1221659.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 1221230.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1222043.0 | 70.0 | EWR | MSY | New Orleans | LA |
| 1230719.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1231659.0 | 94.0 | EWR | MSY | New Orleans | LA |
| 1231230.0 | 111.0 | EWR | MSY | New Orleans | LA |
| 1232043.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 1240719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1241659.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1241230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1242043.0 | 56.0 | EWR | MSY | New Orleans | LA |
| 1251230.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1250719.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 1261654.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 1261230.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1260709.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1262043.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 1271659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1270719.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1281230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1280719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1281730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1282043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1291659.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 1290719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1292043.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 1300719.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1301659.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 1301230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1302043.0 | 93.0 | EWR | MSY | New Orleans | LA |
| 1310719.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 1311659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1311230.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1312043.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 1171230.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1201250.0 | -12.0 | EWR | MSY | New Orleans | LA |
| 1291230.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1011815.0 | 125.0 | EWR | MSY | New Orleans | LA |
| 1021815.0 | 33.0 | EWR | MSY | New Orleans | LA |
| 1031815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1040755.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1051815.0 | 172.0 | EWR | MSY | New Orleans | LA |
| 1061815.0 | 151.0 | EWR | MSY | New Orleans | LA |
| 1071815.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1081815.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1091815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1101815.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 1110730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1121815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1131815.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1141815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1151815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1161815.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1171815.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1180730.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1191815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1211815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1221815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1231815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1241815.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1250730.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1261815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1271815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1281815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1291815.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1301815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1311815.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2011055.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 2021025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 2031025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 2031750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 2041025.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 2041750.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2051025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2051750.0 | 29.0 | LAS | MSY | New Orleans | LA |
| 2061025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2061750.0 | 48.0 | LAS | MSY | New Orleans | LA |
| 2071025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2071750.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 2081055.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2091025.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2091750.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 2101025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 2101750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 2111025.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2111750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 2121025.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2121750.0 | 158.0 | LAS | MSY | New Orleans | LA |
| 2131855.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2131215.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 2141855.0 | 22.0 | LAS | MSY | New Orleans | LA |
| 2141215.0 | 18.0 | LAS | MSY | New Orleans | LA |
| 2150935.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2151635.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2161855.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2161215.0 | 17.0 | LAS | MSY | New Orleans | LA |
| 2171855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2171215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2181855.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2181215.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2191855.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 2191215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2201855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2201215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 2211855.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2211215.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2220935.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 2221635.0 | 133.0 | LAS | MSY | New Orleans | LA |
| 2231855.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2231215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2241855.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2241215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2251855.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 2251215.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2261855.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2261215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2271855.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2271215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2281855.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 2281215.0 | 94.0 | LAS | MSY | New Orleans | LA |
| 3010935.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3011635.0 | 39.0 | LAS | MSY | New Orleans | LA |
| 3021855.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3021215.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3031855.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 3031215.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 3041855.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3041215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 3051855.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 3051215.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 3061855.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 3061215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3071855.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3071215.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3081940.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 3080830.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 3091905.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3090840.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 3101905.0 | 49.0 | LAS | MSY | New Orleans | LA |
| 3100840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3111905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3110840.0 | 84.0 | LAS | MSY | New Orleans | LA |
| 3121905.0 | 26.0 | LAS | MSY | New Orleans | LA |
| 3120840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3131905.0 | 37.0 | LAS | MSY | New Orleans | LA |
| 3130840.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 3141905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3140840.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3150830.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3151940.0 | 95.0 | LAS | MSY | New Orleans | LA |
| 3161905.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 3160840.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3171905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3170840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3181905.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 3180840.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 3191905.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3190840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3201905.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3200840.0 | 68.0 | LAS | MSY | New Orleans | LA |
| 3211905.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3210840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3220830.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 3221940.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 3231905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3230840.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3241905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3240840.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3251905.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3250840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3261905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3260840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3271905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3270840.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3281905.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3280840.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 3290830.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3291940.0 | 231.0 | LAS | MSY | New Orleans | LA |
| 3301905.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 3300840.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 3311905.0 | 19.0 | LAS | MSY | New Orleans | LA |
| 3310840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1010850.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1011755.0 | 160.0 | LAS | MSY | New Orleans | LA |
| 1021805.0 | 138.0 | LAS | MSY | New Orleans | LA |
| 1020905.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1031805.0 | 154.0 | LAS | MSY | New Orleans | LA |
| 1030905.0 | 179.0 | LAS | MSY | New Orleans | LA |
| 1041655.0 | 113.0 | LAS | MSY | New Orleans | LA |
| 1040900.0 | 56.0 | LAS | MSY | New Orleans | LA |
| 1050905.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1051805.0 | 53.0 | LAS | MSY | New Orleans | LA |
| 1061755.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 1060905.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 1071025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1071750.0 | 302.0 | LAS | MSY | New Orleans | LA |
| 1081025.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 1081750.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 1091025.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1091750.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 1101025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1101750.0 | 92.0 | LAS | MSY | New Orleans | LA |
| 1111055.0 | 31.0 | LAS | MSY | New Orleans | LA |
| 1121025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1121750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1131025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1131750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1141025.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1141750.0 | 127.0 | LAS | MSY | New Orleans | LA |
| 1151025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1151750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1161025.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1161750.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1171025.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 1171750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1181055.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1191025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1191750.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1201025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1201750.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 1211025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1211750.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 1221025.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1221750.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1231025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1231750.0 | 30.0 | LAS | MSY | New Orleans | LA |
| 1241025.0 | 43.0 | LAS | MSY | New Orleans | LA |
| 1241750.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1251055.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1261025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1261750.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 1271025.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1271750.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 1281025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1281750.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1291025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1291750.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1301025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1301750.0 | 35.0 | LAS | MSY | New Orleans | LA |
| 1311025.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 1311750.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 2011530.0 | -3.0 | MCI | MSY | New Orleans | LA |
| 2021105.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 2031105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2041105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 2051105.0 | 6.0 | MCI | MSY | New Orleans | LA |
| 2061105.0 | 40.0 | MCI | MSY | New Orleans | LA |
| 2071105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2081530.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2091105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2101105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2111105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2121105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2130800.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 2140830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2150750.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2160930.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2170830.0 | 10.0 | MCI | MSY | New Orleans | LA |
| 2180830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2190830.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 2200830.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 2210830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2220750.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 2230930.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2240830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2250830.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 2260830.0 | 251.0 | MCI | MSY | New Orleans | LA |
| 2270830.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2280830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3010750.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3020930.0 | 35.0 | MCI | MSY | New Orleans | LA |
| 3030830.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3040830.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 3050830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 3060830.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3070830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3081610.0 | 93.0 | MCI | MSY | New Orleans | LA |
| 3090950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3100950.0 | 8.0 | MCI | MSY | New Orleans | LA |
| 3110950.0 | 36.0 | MCI | MSY | New Orleans | LA |
| 3120950.0 | 68.0 | MCI | MSY | New Orleans | LA |
| 3130950.0 | 13.0 | MCI | MSY | New Orleans | LA |
| 3140950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3151610.0 | 16.0 | MCI | MSY | New Orleans | LA |
| 3160950.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 3170950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3180950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3190950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3200950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 3210950.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 3221610.0 | 38.0 | MCI | MSY | New Orleans | LA |
| 3230950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3240950.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3250950.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 3260950.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 3270950.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3280950.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3291610.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3300950.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3310950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1011635.0 | -7.0 | MCI | MSY | New Orleans | LA |
| 1021635.0 | 96.0 | MCI | MSY | New Orleans | LA |
| 1031635.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1041205.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1051635.0 | 60.0 | MCI | MSY | New Orleans | LA |
| 1061635.0 | -7.0 | MCI | MSY | New Orleans | LA |
| 1071105.0 | 14.0 | MCI | MSY | New Orleans | LA |
| 1081105.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 1091105.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1101105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 1111530.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1121105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1131105.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 1141105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1151105.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 1161105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1171105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 1181530.0 | 48.0 | MCI | MSY | New Orleans | LA |
| 1191105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1201105.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 1211105.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 1221105.0 | 19.0 | MCI | MSY | New Orleans | LA |
| 1231105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 1241105.0 | 72.0 | MCI | MSY | New Orleans | LA |
| 1251530.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 1261105.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 1271105.0 | -6.0 | MCI | MSY | New Orleans | LA |
| 1281105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1291105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1301105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1311105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3011530.0 | 72.0 | BNA | MSY | New Orleans | LA |
| 3010850.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 3011245.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3021610.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 3021350.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3021800.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3031610.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3030810.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3031350.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3031800.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3041610.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3040810.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3041350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3041800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3051610.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3050810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3051350.0 | 48.0 | BNA | MSY | New Orleans | LA |
| 3051800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3061610.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 3060810.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3061350.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3061800.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3071610.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3070810.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3071350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3071800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3081540.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3081335.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3080945.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3091620.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3091820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3091420.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3100820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3101420.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3101820.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3101620.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3110820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3111420.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3111820.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3111620.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3120820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3121420.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3121820.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3121620.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 3130820.0 | 126.0 | BNA | MSY | New Orleans | LA |
| 3131420.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3131820.0 | 55.0 | BNA | MSY | New Orleans | LA |
| 3131620.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3140820.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3141420.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3141820.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3141620.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3151335.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3150945.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3151540.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3161620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3161820.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 3161420.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3170820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3171420.0 | 112.0 | BNA | MSY | New Orleans | LA |
| 3171820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3171620.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3180820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3181420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3181820.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3181620.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3190820.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3191420.0 | 54.0 | BNA | MSY | New Orleans | LA |
| 3191820.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3191620.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3200820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3201420.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 3201820.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 3201620.0 | 79.0 | BNA | MSY | New Orleans | LA |
| 3210820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3211420.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3211820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3211620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3221335.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3220945.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3221540.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3231620.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3231820.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 3231420.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3240820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3241420.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3241820.0 | 44.0 | BNA | MSY | New Orleans | LA |
| 3241620.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3250820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3251420.0 | 70.0 | BNA | MSY | New Orleans | LA |
| 3251820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3251620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3260820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3261420.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 3261820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3261620.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3270820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3271420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3271820.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3271620.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 3280820.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3281420.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 3281820.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 3281620.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3291335.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3290945.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 3291540.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3301620.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3301820.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3301420.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3310820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3311420.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 3311820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3311620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 1010800.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1011210.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1011835.0 | 53.0 | BNA | MSY | New Orleans | LA |
| 1021215.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 1021835.0 | 200.0 | BNA | MSY | New Orleans | LA |
| 1020800.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 1031215.0 | 185.0 | BNA | MSY | New Orleans | LA |
| 1031835.0 | 226.0 | BNA | MSY | New Orleans | LA |
| 1030800.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1041420.0 | 174.0 | BNA | MSY | New Orleans | LA |
| 1040835.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1051215.0 | 85.0 | BNA | MSY | New Orleans | LA |
| 1051835.0 | 283.0 | BNA | MSY | New Orleans | LA |
| 1050800.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1061215.0 | 150.0 | BNA | MSY | New Orleans | LA |
| 1061835.0 | 133.0 | BNA | MSY | New Orleans | LA |
| 1060800.0 | 102.0 | BNA | MSY | New Orleans | LA |
| 1071240.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1071455.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1070905.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1071735.0 | 131.0 | BNA | MSY | New Orleans | LA |
| 1081240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1081455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1080905.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1081735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 1091240.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 1091455.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 1090905.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1091735.0 | 28.0 | BNA | MSY | New Orleans | LA |
| 1101240.0 | 50.0 | BNA | MSY | New Orleans | LA |
| 1101455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 1100905.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1101735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1111305.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1111805.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1110950.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1121235.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1121455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1121735.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1131240.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1131455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1130850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1131735.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 1141240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1141455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1140850.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 1141735.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1151240.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1151455.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 1150850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1151735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1161240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161455.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1160850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1161735.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 1171240.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 1171455.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1170850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1171735.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 1181305.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1181805.0 | 42.0 | BNA | MSY | New Orleans | LA |
| 1180950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191235.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 1191455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191735.0 | 64.0 | BNA | MSY | New Orleans | LA |
| 1201240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1201455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1200850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1201735.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1211240.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 1211455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1210850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1211735.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1221240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1221455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1220850.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 1221735.0 | 118.0 | BNA | MSY | New Orleans | LA |
| 1231240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1231455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1230850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1231735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1241240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1241455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1240850.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1241735.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1251305.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 1251805.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1250950.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1261235.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1261455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1261735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1271240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1271455.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1270850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1271735.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1281240.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1281455.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1280850.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1281735.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1291240.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1291455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1290850.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1291735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1301240.0 | 38.0 | BNA | MSY | New Orleans | LA |
| 1301455.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 1300850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1301735.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1311240.0 | 31.0 | BNA | MSY | New Orleans | LA |
| 1311455.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 1310850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1311735.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2011305.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2011805.0 | -6.0 | BNA | MSY | New Orleans | LA |
| 2010950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 2021455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2021235.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2021650.0 | 77.0 | BNA | MSY | New Orleans | LA |
| 2031240.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 2031455.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2030850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2031735.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 2041240.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 2041455.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2040850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2041735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2051240.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 2051455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2050850.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 2051735.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2061240.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 2061455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2060850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2061735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 2071240.0 | 88.0 | BNA | MSY | New Orleans | LA |
| 2071455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2070850.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2071735.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 2081305.0 | -6.0 | BNA | MSY | New Orleans | LA |
| 2081805.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 2080950.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 2091235.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2091455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2091735.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2101240.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2101455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2100850.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2101735.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2111240.0 | 145.0 | BNA | MSY | New Orleans | LA |
| 2111455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2110850.0 | 96.0 | BNA | MSY | New Orleans | LA |
| 2111735.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2121240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2121455.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 2120850.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2121735.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 2131350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2131610.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2130810.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 2131800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2141610.0 | 37.0 | BNA | MSY | New Orleans | LA |
| 2140810.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 2141350.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2141800.0 | 21.0 | BNA | MSY | New Orleans | LA |
// Build `tripGraph` GraphFrame
// This GraphFrame builds up on the vertices and edges based on our trips (flights)
val tripGraph = GraphFrame(tripVertices, tripEdges)
println(tripGraph)
// Build `tripGraphPrime` GraphFrame
// This graphframe contains a smaller subset of data to make it easier to display motifs and subgraphs (below)
val tripEdgesPrime = departureDelays_geo.select("tripid", "delay", "src", "dst")
val tripGraphPrime = GraphFrame(tripVertices, tripEdgesPrime)
GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripGraph: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripEdgesPrime: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 2 more fields]
tripGraphPrime: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 2 more fields])
Simple Queries
Let's start with a set of simple graph queries to understand flight performance and departure delays
println(s"Airports: ${tripGraph.vertices.count()}")
println(s"Trips: ${tripGraph.edges.count()}")
Airports: 279
Trips: 1361141
// Finding the longest Delay
val longestDelay = tripGraph.edges.groupBy().max("delay")
display(longestDelay)
| max(delay) |
|---|
| 1642.0 |
1642.0/60.0
res13: Double = 27.366666666666667
// Determining number of on-time / early flights vs. delayed flights
println(s"On-time / Early Flights: ${tripGraph.edges.filter("delay <= 0").count()}")
println(s"Delayed Flights: ${tripGraph.edges.filter("delay > 0").count()}")
On-time / Early Flights: 780469
Delayed Flights: 580672
What flights departing SFO are most likely to have significant delays
Note, delay can be <= 0 meaning the flight left on time or early
//tripGraph.createOrReplaceTempView("tripgraph")
val sfoDelayedTrips = tripGraph.edges.
filter("src = 'SFO' and delay > 0").
groupBy("src", "dst").
avg("delay").
sort(desc("avg(delay)"))
sfoDelayedTrips: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
display(sfoDelayedTrips)
| src | dst | avg(delay) |
|---|---|---|
| SFO | OKC | 59.073170731707314 |
| SFO | JAC | 57.13333333333333 |
| SFO | COS | 53.976190476190474 |
| SFO | OTH | 48.09090909090909 |
| SFO | SAT | 47.625 |
| SFO | MOD | 46.80952380952381 |
| SFO | SUN | 46.723404255319146 |
| SFO | CIC | 46.72164948453608 |
| SFO | ABQ | 44.8125 |
| SFO | ASE | 44.285714285714285 |
| SFO | PIT | 43.875 |
| SFO | MIA | 43.81730769230769 |
| SFO | FAT | 43.23972602739726 |
| SFO | MFR | 43.11848341232228 |
| SFO | SBP | 43.09770114942529 |
| SFO | MSP | 42.766917293233085 |
| SFO | BOI | 42.65482233502538 |
| SFO | RDM | 41.98823529411764 |
| SFO | AUS | 41.690677966101696 |
| SFO | SLC | 41.407272727272726 |
| SFO | JFK | 41.01379310344828 |
| SFO | PSP | 40.909909909909906 |
| SFO | PHX | 40.67272727272727 |
| SFO | MRY | 40.61764705882353 |
| SFO | ACV | 40.3728813559322 |
| SFO | LAS | 40.107602339181284 |
| SFO | TUS | 39.853658536585364 |
| SFO | SAN | 38.97361809045226 |
| SFO | SBA | 38.758620689655174 |
| SFO | BFL | 38.51136363636363 |
| SFO | RDU | 38.170731707317074 |
| SFO | STL | 38.13513513513514 |
| SFO | IND | 38.114285714285714 |
| SFO | EUG | 37.573913043478264 |
| SFO | RNO | 36.81372549019608 |
| SFO | BUR | 36.75675675675676 |
| SFO | LGB | 36.752941176470586 |
| SFO | HNL | 36.25367647058823 |
| SFO | LAX | 36.165543071161046 |
| SFO | RDD | 36.11009174311926 |
| SFO | MSY | 35.421052631578945 |
| SFO | SMF | 34.936 |
| SFO | MDW | 34.824742268041234 |
| SFO | FLL | 34.76842105263158 |
| SFO | SEA | 34.68854961832061 |
| SFO | MCI | 34.68571428571428 |
| SFO | DFW | 34.36642599277978 |
| SFO | OGG | 34.171875 |
| SFO | PDX | 34.14430894308943 |
| SFO | ORD | 33.991130820399114 |
| SFO | LIH | 32.93023255813954 |
| SFO | DEN | 32.861491628614914 |
| SFO | PSC | 32.604651162790695 |
| SFO | PHL | 32.440677966101696 |
| SFO | BWI | 31.70212765957447 |
| SFO | ONT | 31.49079754601227 |
| SFO | SNA | 31.18426103646833 |
| SFO | MCO | 31.03488372093023 |
| SFO | MKE | 31.03448275862069 |
| SFO | CLE | 30.979591836734695 |
| SFO | EWR | 30.354285714285716 |
| SFO | BOS | 29.623471882640587 |
| SFO | LMT | 29.233333333333334 |
| SFO | DTW | 28.34722222222222 |
| SFO | IAH | 28.322105263157894 |
| SFO | CVG | 27.03125 |
| SFO | ATL | 26.84860557768924 |
| SFO | IAD | 26.125964010282775 |
| SFO | ANC | 25.5 |
| SFO | BZN | 23.964285714285715 |
| SFO | CLT | 22.636363636363637 |
| SFO | DCA | 21.896103896103895 |
// After displaying tripDelays, use Plot Options to set `state_dst` as a Key.
val tripDelays = tripGraph.edges.filter($"delay" > 0)
display(tripDelays)
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 1021111.0 | 7.0 | MSP | INL | International Falls | MN |
| 1061115.0 | 33.0 | MSP | INL | International Falls | MN |
| 1071115.0 | 23.0 | MSP | INL | International Falls | MN |
| 1091115.0 | 11.0 | MSP | INL | International Falls | MN |
| 1171115.0 | 4.0 | MSP | INL | International Falls | MN |
| 2091925.0 | 1.0 | MSP | INL | International Falls | MN |
| 2152015.0 | 16.0 | MSP | INL | International Falls | MN |
| 2161925.0 | 169.0 | MSP | INL | International Falls | MN |
| 2171115.0 | 27.0 | MSP | INL | International Falls | MN |
| 2181115.0 | 96.0 | MSP | INL | International Falls | MN |
| 2281115.0 | 5.0 | MSP | INL | International Falls | MN |
| 3031115.0 | 17.0 | MSP | INL | International Falls | MN |
| 3171115.0 | 25.0 | MSP | INL | International Falls | MN |
| 3181115.0 | 2.0 | MSP | INL | International Falls | MN |
| 3271115.0 | 9.0 | MSP | INL | International Falls | MN |
| 2020709.0 | 59.0 | EWR | MSY | New Orleans | LA |
| 2021654.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2041230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 2040719.0 | 168.0 | EWR | MSY | New Orleans | LA |
| 2041730.0 | 88.0 | EWR | MSY | New Orleans | LA |
| 2042043.0 | 106.0 | EWR | MSY | New Orleans | LA |
| 2052043.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2061659.0 | 82.0 | EWR | MSY | New Orleans | LA |
| 2061230.0 | 61.0 | EWR | MSY | New Orleans | LA |
| 2062043.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2070719.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2071659.0 | 19.0 | EWR | MSY | New Orleans | LA |
| 2071230.0 | 27.0 | EWR | MSY | New Orleans | LA |
| 2072048.0 | 47.0 | EWR | MSY | New Orleans | LA |
| 2091229.0 | 95.0 | EWR | MSY | New Orleans | LA |
| 2092043.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2100719.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 2101659.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 2111230.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2110719.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2120719.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 2120929.0 | 89.0 | EWR | MSY | New Orleans | LA |
| 2122043.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2182041.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2190727.0 | 15.0 | EWR | MSY | New Orleans | LA |
| 2202041.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 2220659.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2232041.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2240729.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2260727.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2262041.0 | 174.0 | EWR | MSY | New Orleans | LA |
| 2270738.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2272041.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2280729.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 2282041.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 2281000.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 2051230.0 | 216.0 | EWR | MSY | New Orleans | LA |
| 2131536.0 | 273.0 | EWR | MSY | New Orleans | LA |
| 2141536.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2151902.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 2151536.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 2161536.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2171536.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 2181536.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 2211536.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 2221536.0 | 65.0 | EWR | MSY | New Orleans | LA |
| 2281536.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2111815.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 2121815.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 2141635.0 | 52.0 | EWR | MSY | New Orleans | LA |
| 2161635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2171635.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2191635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2211635.0 | 292.0 | EWR | MSY | New Orleans | LA |
| 2220730.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 2251635.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2261635.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3032041.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 3052041.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 3061252.0 | 67.0 | EWR | MSY | New Orleans | LA |
| 3062100.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 3072100.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 3081250.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3112059.0 | 181.0 | EWR | MSY | New Orleans | LA |
| 3122059.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3121252.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 3140705.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 3141252.0 | 39.0 | EWR | MSY | New Orleans | LA |
| 3150700.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3151250.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 3171252.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3172059.0 | 132.0 | EWR | MSY | New Orleans | LA |
| 3181252.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3192059.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3202059.0 | 77.0 | EWR | MSY | New Orleans | LA |
| 3211252.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3212059.0 | 11.0 | EWR | MSY | New Orleans | LA |
| 3231255.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3252059.0 | 121.0 | EWR | MSY | New Orleans | LA |
| 3262059.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 3291600.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3301255.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3302059.0 | 166.0 | EWR | MSY | New Orleans | LA |
| 3311252.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 3312059.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 3011902.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3021536.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3090715.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3260659.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 3300715.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3021635.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3041635.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 3071635.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3091825.0 | 45.0 | EWR | MSY | New Orleans | LA |
| 3101825.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3111825.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3131825.0 | 123.0 | EWR | MSY | New Orleans | LA |
| 3141825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3161825.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 3171825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3191825.0 | 223.0 | EWR | MSY | New Orleans | LA |
| 3201825.0 | 178.0 | EWR | MSY | New Orleans | LA |
| 3251825.0 | 222.0 | EWR | MSY | New Orleans | LA |
| 3261825.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 3281825.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 3301825.0 | 139.0 | EWR | MSY | New Orleans | LA |
| 3311825.0 | 25.0 | EWR | MSY | New Orleans | LA |
| 1050703.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 1060705.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 1071230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 1071730.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 1072043.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1081230.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 1082043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1092043.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 1101659.0 | 244.0 | EWR | MSY | New Orleans | LA |
| 1101230.0 | 110.0 | EWR | MSY | New Orleans | LA |
| 1102043.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1111230.0 | 87.0 | EWR | MSY | New Orleans | LA |
| 1121230.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 1131230.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1132043.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 1141230.0 | 30.0 | EWR | MSY | New Orleans | LA |
| 1141730.0 | 69.0 | EWR | MSY | New Orleans | LA |
| 1150719.0 | 42.0 | EWR | MSY | New Orleans | LA |
| 1151659.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1152043.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1162043.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1171659.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 1172043.0 | 54.0 | EWR | MSY | New Orleans | LA |
| 1181230.0 | 20.0 | EWR | MSY | New Orleans | LA |
| 1191230.0 | 29.0 | EWR | MSY | New Orleans | LA |
| 1192043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1202043.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1211230.0 | 102.0 | EWR | MSY | New Orleans | LA |
| 1221659.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 1222043.0 | 70.0 | EWR | MSY | New Orleans | LA |
| 1230719.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1231659.0 | 94.0 | EWR | MSY | New Orleans | LA |
| 1231230.0 | 111.0 | EWR | MSY | New Orleans | LA |
| 1232043.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 1241659.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1242043.0 | 56.0 | EWR | MSY | New Orleans | LA |
| 1250719.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 1261654.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 1261230.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1262043.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 1300719.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1301659.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 1302043.0 | 93.0 | EWR | MSY | New Orleans | LA |
| 1310719.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 1311230.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1312043.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 1011815.0 | 125.0 | EWR | MSY | New Orleans | LA |
| 1021815.0 | 33.0 | EWR | MSY | New Orleans | LA |
| 1051815.0 | 172.0 | EWR | MSY | New Orleans | LA |
| 1061815.0 | 151.0 | EWR | MSY | New Orleans | LA |
| 1071815.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1081815.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1091815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1101815.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 1141815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1151815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1161815.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1171815.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1180730.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1191815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1221815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1241815.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 2021025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 2041025.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 2051750.0 | 29.0 | LAS | MSY | New Orleans | LA |
| 2061025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2061750.0 | 48.0 | LAS | MSY | New Orleans | LA |
| 2071750.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 2081055.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2091750.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 2111025.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2121750.0 | 158.0 | LAS | MSY | New Orleans | LA |
| 2131855.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2131215.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 2141855.0 | 22.0 | LAS | MSY | New Orleans | LA |
| 2141215.0 | 18.0 | LAS | MSY | New Orleans | LA |
| 2151635.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2161855.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2161215.0 | 17.0 | LAS | MSY | New Orleans | LA |
| 2171855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2171215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2181855.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2181215.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2191215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2201855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2201215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 2211855.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2211215.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2221635.0 | 133.0 | LAS | MSY | New Orleans | LA |
| 2231215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2241215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2251855.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 2251215.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2261855.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2261215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2271855.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2271215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2281855.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 2281215.0 | 94.0 | LAS | MSY | New Orleans | LA |
| 3010935.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3011635.0 | 39.0 | LAS | MSY | New Orleans | LA |
| 3031855.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 3031215.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 3041215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 3051215.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 3061855.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 3061215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3071855.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3071215.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3080830.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 3091905.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3101905.0 | 49.0 | LAS | MSY | New Orleans | LA |
| 3111905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3110840.0 | 84.0 | LAS | MSY | New Orleans | LA |
| 3121905.0 | 26.0 | LAS | MSY | New Orleans | LA |
| 3131905.0 | 37.0 | LAS | MSY | New Orleans | LA |
| 3130840.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 3141905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3150830.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3151940.0 | 95.0 | LAS | MSY | New Orleans | LA |
| 3171905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3170840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3181905.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 3180840.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 3201905.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3200840.0 | 68.0 | LAS | MSY | New Orleans | LA |
| 3220830.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 3221940.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 3231905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3230840.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3241905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3240840.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3251905.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3261905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3260840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3271905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3280840.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 3291940.0 | 231.0 | LAS | MSY | New Orleans | LA |
| 3301905.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 3300840.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 3311905.0 | 19.0 | LAS | MSY | New Orleans | LA |
| 1011755.0 | 160.0 | LAS | MSY | New Orleans | LA |
| 1021805.0 | 138.0 | LAS | MSY | New Orleans | LA |
| 1020905.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1031805.0 | 154.0 | LAS | MSY | New Orleans | LA |
| 1030905.0 | 179.0 | LAS | MSY | New Orleans | LA |
| 1041655.0 | 113.0 | LAS | MSY | New Orleans | LA |
| 1040900.0 | 56.0 | LAS | MSY | New Orleans | LA |
| 1051805.0 | 53.0 | LAS | MSY | New Orleans | LA |
| 1061755.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 1060905.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 1071025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1071750.0 | 302.0 | LAS | MSY | New Orleans | LA |
| 1081025.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 1081750.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 1091750.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 1101750.0 | 92.0 | LAS | MSY | New Orleans | LA |
| 1111055.0 | 31.0 | LAS | MSY | New Orleans | LA |
| 1121025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1141750.0 | 127.0 | LAS | MSY | New Orleans | LA |
| 1151025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1171025.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 1191025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1201750.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 1211025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1211750.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 1221750.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1231750.0 | 30.0 | LAS | MSY | New Orleans | LA |
| 1241025.0 | 43.0 | LAS | MSY | New Orleans | LA |
| 1251055.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1261750.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 1271750.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 1291025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1301750.0 | 35.0 | LAS | MSY | New Orleans | LA |
| 1311025.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 1311750.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 2041105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 2051105.0 | 6.0 | MCI | MSY | New Orleans | LA |
| 2061105.0 | 40.0 | MCI | MSY | New Orleans | LA |
| 2071105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2081530.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2121105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2140830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2150750.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2170830.0 | 10.0 | MCI | MSY | New Orleans | LA |
| 2180830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2200830.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 2230930.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2240830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2260830.0 | 251.0 | MCI | MSY | New Orleans | LA |
| 3020930.0 | 35.0 | MCI | MSY | New Orleans | LA |
| 3030830.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3040830.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 3050830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 3081610.0 | 93.0 | MCI | MSY | New Orleans | LA |
| 3100950.0 | 8.0 | MCI | MSY | New Orleans | LA |
| 3110950.0 | 36.0 | MCI | MSY | New Orleans | LA |
| 3120950.0 | 68.0 | MCI | MSY | New Orleans | LA |
| 3130950.0 | 13.0 | MCI | MSY | New Orleans | LA |
| 3140950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3151610.0 | 16.0 | MCI | MSY | New Orleans | LA |
| 3180950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3200950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 3210950.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 3221610.0 | 38.0 | MCI | MSY | New Orleans | LA |
| 3250950.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 3260950.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 3270950.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3291610.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3310950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1021635.0 | 96.0 | MCI | MSY | New Orleans | LA |
| 1031635.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1041205.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1051635.0 | 60.0 | MCI | MSY | New Orleans | LA |
| 1071105.0 | 14.0 | MCI | MSY | New Orleans | LA |
| 1081105.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 1091105.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1111530.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1141105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1151105.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 1161105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1181530.0 | 48.0 | MCI | MSY | New Orleans | LA |
| 1211105.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 1221105.0 | 19.0 | MCI | MSY | New Orleans | LA |
| 1241105.0 | 72.0 | MCI | MSY | New Orleans | LA |
| 1251530.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 1301105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1311105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3011530.0 | 72.0 | BNA | MSY | New Orleans | LA |
| 3010850.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 3011245.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3021610.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 3021350.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3021800.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3031610.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3041350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3041800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3050810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3051350.0 | 48.0 | BNA | MSY | New Orleans | LA |
| 3051800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3061610.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 3061350.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3061800.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3071610.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3071350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3071800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3081540.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3080945.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3091820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3091420.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3101420.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3101820.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3101620.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3110820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3111420.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3111820.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3111620.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3120820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3121420.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3121820.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3121620.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 3130820.0 | 126.0 | BNA | MSY | New Orleans | LA |
| 3131420.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3131820.0 | 55.0 | BNA | MSY | New Orleans | LA |
| 3131620.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3141420.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3141820.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3141620.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3150945.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3161620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3161820.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 3170820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3171420.0 | 112.0 | BNA | MSY | New Orleans | LA |
| 3171820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3171620.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3181420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3181820.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3181620.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3191420.0 | 54.0 | BNA | MSY | New Orleans | LA |
| 3191820.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3191620.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3201420.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 3201820.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 3201620.0 | 79.0 | BNA | MSY | New Orleans | LA |
| 3210820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3211420.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3211820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3211620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3231820.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 3241420.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3241820.0 | 44.0 | BNA | MSY | New Orleans | LA |
| 3241620.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3251420.0 | 70.0 | BNA | MSY | New Orleans | LA |
| 3251820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3251620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3261420.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 3261820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3271420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3271820.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3271620.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 3281420.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 3281820.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 3281620.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3291335.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3290945.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 3291540.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3301820.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3301420.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3311420.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 3311820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3311620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 1011210.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1011835.0 | 53.0 | BNA | MSY | New Orleans | LA |
| 1021215.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 1021835.0 | 200.0 | BNA | MSY | New Orleans | LA |
| 1020800.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 1031215.0 | 185.0 | BNA | MSY | New Orleans | LA |
| 1031835.0 | 226.0 | BNA | MSY | New Orleans | LA |
| 1041420.0 | 174.0 | BNA | MSY | New Orleans | LA |
| 1040835.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1051215.0 | 85.0 | BNA | MSY | New Orleans | LA |
| 1051835.0 | 283.0 | BNA | MSY | New Orleans | LA |
| 1050800.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1061215.0 | 150.0 | BNA | MSY | New Orleans | LA |
| 1061835.0 | 133.0 | BNA | MSY | New Orleans | LA |
| 1060800.0 | 102.0 | BNA | MSY | New Orleans | LA |
| 1071240.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1071455.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1070905.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1071735.0 | 131.0 | BNA | MSY | New Orleans | LA |
| 1081240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1081455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1081735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 1091240.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 1091455.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 1091735.0 | 28.0 | BNA | MSY | New Orleans | LA |
| 1101240.0 | 50.0 | BNA | MSY | New Orleans | LA |
| 1101455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 1100905.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1101735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1111805.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1121235.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1121455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1121735.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1131240.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1131455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1131735.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 1141240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1141735.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1151455.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 1151735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1161240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161455.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161735.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 1171240.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 1171455.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1170850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1171735.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 1181305.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1181805.0 | 42.0 | BNA | MSY | New Orleans | LA |
| 1180950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191235.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 1191455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191735.0 | 64.0 | BNA | MSY | New Orleans | LA |
| 1201240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1201455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1200850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1211240.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 1210850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1211735.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1221240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1221735.0 | 118.0 | BNA | MSY | New Orleans | LA |
| 1231240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1231455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1230850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1231735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1241240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1241455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1240850.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1251305.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 1251805.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1261455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1261735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1271240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1271735.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1291240.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1291735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1301240.0 | 38.0 | BNA | MSY | New Orleans | LA |
| 1301455.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 1301735.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1311240.0 | 31.0 | BNA | MSY | New Orleans | LA |
| 1311455.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 1311735.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2010950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 2021455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2021235.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2021650.0 | 77.0 | BNA | MSY | New Orleans | LA |
| 2031240.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 2031455.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2041240.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 2041455.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2041735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2051240.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 2051455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2050850.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 2051735.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2061240.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 2060850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2061735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 2071240.0 | 88.0 | BNA | MSY | New Orleans | LA |
| 2071455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2070850.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2071735.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 2091235.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2091455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2091735.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2101240.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2101455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2100850.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2111240.0 | 145.0 | BNA | MSY | New Orleans | LA |
| 2110850.0 | 96.0 | BNA | MSY | New Orleans | LA |
| 2111735.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2121240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2121455.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 2120850.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2121735.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 2131350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2131610.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2131800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2141610.0 | 37.0 | BNA | MSY | New Orleans | LA |
| 2140810.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 2141350.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2141800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 2151530.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 2150850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2161800.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 2170810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2171350.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2180810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2181350.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 2181800.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2191350.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2191800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 2201610.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2200810.0 | 16.0 | BNA | MSY | New Orleans | LA |
| 2201350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2201800.0 | 162.0 | BNA | MSY | New Orleans | LA |
| 2211610.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2210810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2211350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2211800.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2221530.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2231350.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2231800.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2241350.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 2250810.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2251800.0 | 103.0 | BNA | MSY | New Orleans | LA |
| 2261610.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2260810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2261350.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2261800.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2271800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2281610.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2281350.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2281800.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3011117.0 | 36.0 | CLT | MSY | New Orleans | LA |
| 3011635.0 | 77.0 | CLT | MSY | New Orleans | LA |
| 3021117.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 3031825.0 | 9.0 | CLT | MSY | New Orleans | LA |
| 3061825.0 | 32.0 | CLT | MSY | New Orleans | LA |
| 3062010.0 | 33.0 | CLT | MSY | New Orleans | LA |
| 3070750.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3071435.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 3081115.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3091115.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3090909.0 | 118.0 | CLT | MSY | New Orleans | LA |
| 3102010.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 3110750.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 3121115.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 3122010.0 | 31.0 | CLT | MSY | New Orleans | LA |
| 3121435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3141825.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3141115.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3140915.0 | 17.0 | CLT | MSY | New Orleans | LA |
| 3161840.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 3162010.0 | 34.0 | CLT | MSY | New Orleans | LA |
| 3161435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3171825.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 3171115.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3172010.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3171435.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3170915.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3180750.0 | 46.0 | CLT | MSY | New Orleans | LA |
| 3182010.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3180915.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3190915.0 | 65.0 | CLT | MSY | New Orleans | LA |
| 3202010.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 3201435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3210750.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3230909.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 3241115.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 3240915.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 3251435.0 | 56.0 | CLT | MSY | New Orleans | LA |
| 3250915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3261115.0 | 9.0 | CLT | MSY | New Orleans | LA |
| 3261435.0 | 96.0 | CLT | MSY | New Orleans | LA |
| 3260915.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3271115.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3270915.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 3281115.0 | 37.0 | CLT | MSY | New Orleans | LA |
| 3282010.0 | 46.0 | CLT | MSY | New Orleans | LA |
| 3291825.0 | 150.0 | CLT | MSY | New Orleans | LA |
| 3291115.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3292010.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 3291635.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3290915.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3311115.0 | 18.0 | CLT | MSY | New Orleans | LA |
| 3311435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1011815.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 1011435.0 | 40.0 | CLT | MSY | New Orleans | LA |
| 1021815.0 | 22.0 | CLT | MSY | New Orleans | LA |
| 1022015.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1021435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1042020.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 1041435.0 | 15.0 | CLT | MSY | New Orleans | LA |
| 1051815.0 | 45.0 | CLT | MSY | New Orleans | LA |
| 1052015.0 | 19.0 | CLT | MSY | New Orleans | LA |
| 1051435.0 | 60.0 | CLT | MSY | New Orleans | LA |
| 1060750.0 | 7.0 | CLT | MSY | New Orleans | LA |
| 1061120.0 | 15.0 | CLT | MSY | New Orleans | LA |
| 1071825.0 | 58.0 | CLT | MSY | New Orleans | LA |
| 1071435.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 1081435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1080915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1091117.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1100750.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 1101825.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 1101117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1101635.0 | 55.0 | CLT | MSY | New Orleans | LA |
| 1101435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1110750.0 | 115.0 | CLT | MSY | New Orleans | LA |
| 1111117.0 | 49.0 | CLT | MSY | New Orleans | LA |
| 1111635.0 | 145.0 | CLT | MSY | New Orleans | LA |
| 1112010.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1111435.0 | 126.0 | CLT | MSY | New Orleans | LA |
| 1110915.0 | 72.0 | CLT | MSY | New Orleans | LA |
| 1121117.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 1121435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1131435.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 1151117.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 1151435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1171117.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 1181117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 1180915.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 1191117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1200750.0 | 51.0 | CLT | MSY | New Orleans | LA |
| 1211825.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1211435.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 1221117.0 | 10.0 | CLT | MSY | New Orleans | LA |
| 1221435.0 | 25.0 | CLT | MSY | New Orleans | LA |
| 1230750.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 1231117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1231635.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 1230915.0 | 131.0 | CLT | MSY | New Orleans | LA |
| 1241117.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 1252010.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1250915.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1271825.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1271117.0 | 52.0 | CLT | MSY | New Orleans | LA |
| 1290750.0 | 26.0 | CLT | MSY | New Orleans | LA |
| 1291117.0 | 19.0 | CLT | MSY | New Orleans | LA |
| 1291635.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 1291435.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 1290915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1311825.0 | 50.0 | CLT | MSY | New Orleans | LA |
| 1310915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 2011117.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 2011635.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 2010900.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2031117.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 2031435.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 2041117.0 | 22.0 | CLT | MSY | New Orleans | LA |
| 2051117.0 | 36.0 | CLT | MSY | New Orleans | LA |
| 2051435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 2050915.0 | 7.0 | CLT | MSY | New Orleans | LA |
| 2060750.0 | 34.0 | CLT | MSY | New Orleans | LA |
| 2061117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 2061635.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 2071825.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 2090915.0 | 79.0 | CLT | MSY | New Orleans | LA |
| 2101435.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 2121635.0 | 42.0 | CLT | MSY | New Orleans | LA |
| 2121435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 2140750.0 | 151.0 | CLT | MSY | New Orleans | LA |
| 2141825.0 | 33.0 | CLT | MSY | New Orleans | LA |
| 2142010.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 2141435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 2140915.0 | 174.0 | CLT | MSY | New Orleans | LA |
| 2150915.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 2161117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 2161435.0 | 12.0 | CLT | MSY | New Orleans | LA |
| 2171117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2171435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2170915.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 2200915.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 2211117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2211435.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 2210915.0 | 52.0 | CLT | MSY | New Orleans | LA |
| 2232010.0 | 74.0 | CLT | MSY | New Orleans | LA |
| 2251435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 2261825.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 2272010.0 | 10.0 | CLT | MSY | New Orleans | LA |
| 2271435.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 2280750.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2281117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3011715.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3011830.0 | 124.0 | DAL | MSY | New Orleans | LA |
| 3021710.0 | 174.0 | DAL | MSY | New Orleans | LA |
| 3021335.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3022025.0 | 84.0 | DAL | MSY | New Orleans | LA |
| 3021855.0 | 55.0 | DAL | MSY | New Orleans | LA |
| 3030605.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3031335.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3032025.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3030920.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3031710.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3031100.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3031855.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3041335.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3042025.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3052025.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3051710.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 3051855.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3061335.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 3062025.0 | 52.0 | DAL | MSY | New Orleans | LA |
| 3060920.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3061710.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3061100.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3070805.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3071335.0 | 60.0 | DAL | MSY | New Orleans | LA |
| 3072025.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 3071710.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 3071100.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3071855.0 | 66.0 | DAL | MSY | New Orleans | LA |
| 3081440.0 | 107.0 | DAL | MSY | New Orleans | LA |
| 3081745.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3091900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3092055.0 | 35.0 | DAL | MSY | New Orleans | LA |
| 3091810.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3091455.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3101900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3102055.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 3101810.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3111900.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3112055.0 | 103.0 | DAL | MSY | New Orleans | LA |
| 3111205.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3110825.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3111810.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3111455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3121900.0 | 50.0 | DAL | MSY | New Orleans | LA |
| 3122055.0 | 57.0 | DAL | MSY | New Orleans | LA |
| 3121205.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3120825.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3121810.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3131900.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 3130930.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3132055.0 | 77.0 | DAL | MSY | New Orleans | LA |
| 3130600.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3130825.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3131810.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3141900.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 3142055.0 | 25.0 | DAL | MSY | New Orleans | LA |
| 3140600.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3141205.0 | 54.0 | DAL | MSY | New Orleans | LA |
| 3141810.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3141455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3150830.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3151440.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3161900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3162055.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3161810.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3160930.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3171900.0 | 83.0 | DAL | MSY | New Orleans | LA |
| 3170930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3172055.0 | 81.0 | DAL | MSY | New Orleans | LA |
| 3170825.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3171810.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3181900.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 3182055.0 | 148.0 | DAL | MSY | New Orleans | LA |
| 3180600.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3181205.0 | 32.0 | DAL | MSY | New Orleans | LA |
| 3180825.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3181810.0 | 92.0 | DAL | MSY | New Orleans | LA |
| 3181455.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3191900.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3192055.0 | 71.0 | DAL | MSY | New Orleans | LA |
| 3191205.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3191810.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3191455.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3201900.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 3202055.0 | 34.0 | DAL | MSY | New Orleans | LA |
| 3200600.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3201205.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3200825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3201810.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 3211900.0 | 52.0 | DAL | MSY | New Orleans | LA |
| 3212055.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3210600.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3211205.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3210825.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3211810.0 | 14.0 | DAL | MSY | New Orleans | LA |
| 3211455.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3221750.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3221440.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3231900.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3231810.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3231455.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3231210.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3231045.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3230930.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3241900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3242055.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3241205.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3240825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3241810.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3241455.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3251900.0 | 58.0 | DAL | MSY | New Orleans | LA |
| 3252055.0 | 51.0 | DAL | MSY | New Orleans | LA |
| 3251205.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3250825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3251810.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3251455.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3261900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3262055.0 | 69.0 | DAL | MSY | New Orleans | LA |
| 3261205.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3260825.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3261810.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3261455.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3271900.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3272055.0 | 78.0 | DAL | MSY | New Orleans | LA |
| 3271205.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3270825.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3271810.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 3271455.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3281900.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 3280930.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3282055.0 | 65.0 | DAL | MSY | New Orleans | LA |
| 3281205.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3280825.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3281810.0 | 49.0 | DAL | MSY | New Orleans | LA |
| 3281455.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3290830.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3291440.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3301900.0 | 126.0 | DAL | MSY | New Orleans | LA |
| 3301810.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3301455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3301045.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3300930.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3311900.0 | 47.0 | DAL | MSY | New Orleans | LA |
| 3310930.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3312055.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3311205.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3310825.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 3311810.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 3311455.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1012115.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1011235.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1011650.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 1011525.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 1011120.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1021120.0 | 40.0 | DAL | MSY | New Orleans | LA |
| 1021650.0 | 37.0 | DAL | MSY | New Orleans | LA |
| 1020925.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1022110.0 | 115.0 | DAL | MSY | New Orleans | LA |
| 1021235.0 | 66.0 | DAL | MSY | New Orleans | LA |
| 1020605.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 1021525.0 | 91.0 | DAL | MSY | New Orleans | LA |
| 1021910.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1031120.0 | 121.0 | DAL | MSY | New Orleans | LA |
| 1031650.0 | 154.0 | DAL | MSY | New Orleans | LA |
| 1030925.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 1032110.0 | 120.0 | DAL | MSY | New Orleans | LA |
| 1031235.0 | 43.0 | DAL | MSY | New Orleans | LA |
| 1031525.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 1031910.0 | 194.0 | DAL | MSY | New Orleans | LA |
| 1040710.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1041730.0 | 108.0 | DAL | MSY | New Orleans | LA |
| 1041420.0 | 49.0 | DAL | MSY | New Orleans | LA |
| 1040930.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 1041540.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1052110.0 | 110.0 | DAL | MSY | New Orleans | LA |
| 1051235.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1051525.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1051150.0 | 32.0 | DAL | MSY | New Orleans | LA |
| 1051910.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1050925.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 1051650.0 | 101.0 | DAL | MSY | New Orleans | LA |
| 1061120.0 | 48.0 | DAL | MSY | New Orleans | LA |
| 1061650.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1062110.0 | 103.0 | DAL | MSY | New Orleans | LA |
| 1061235.0 | 71.0 | DAL | MSY | New Orleans | LA |
| 1061525.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 1072015.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 1071535.0 | 53.0 | DAL | MSY | New Orleans | LA |
| 1070630.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1071930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1070925.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1071025.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 1071230.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1071635.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1082015.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 1081535.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 1081930.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 1080925.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1081025.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1081230.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1081635.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1092015.0 | 89.0 | DAL | MSY | New Orleans | LA |
| 1091535.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 1090925.0 | 39.0 | DAL | MSY | New Orleans | LA |
| 1091025.0 | 94.0 | DAL | MSY | New Orleans | LA |
| 1091230.0 | 73.0 | DAL | MSY | New Orleans | LA |
| 1091635.0 | 35.0 | DAL | MSY | New Orleans | LA |
| 1102015.0 | 100.0 | DAL | MSY | New Orleans | LA |
| 1101535.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 1101930.0 | 118.0 | DAL | MSY | New Orleans | LA |
| 1100925.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1101025.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 1101230.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 1101635.0 | 57.0 | DAL | MSY | New Orleans | LA |
| 1111750.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 1110645.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1111450.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1111625.0 | 50.0 | DAL | MSY | New Orleans | LA |
| 1122015.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1121535.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1121620.0 | 33.0 | DAL | MSY | New Orleans | LA |
| 1121930.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1120925.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1121230.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1131535.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1130630.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1130925.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 1131025.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 1142015.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1140630.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1140925.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1141025.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 1141230.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1141635.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1151930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1150925.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1151025.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 1162015.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1161535.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 1161930.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1160925.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1161025.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1161230.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1161635.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 1172015.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1171535.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1171025.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1171230.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1171635.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 1180645.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 1181450.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1181625.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 1191535.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1191620.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1191930.0 | 219.0 | DAL | MSY | New Orleans | LA |
| 1190925.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1200925.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1201025.0 | 33.0 | DAL | MSY | New Orleans | LA |
| 1201230.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1201635.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1211535.0 | 95.0 | DAL | MSY | New Orleans | LA |
// States with the longest cumulative delays (with individual delays > 100 minutes) (origin: Seattle)
display(tripGraph.edges.filter($"src" === "SEA" && $"delay" > 100))
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 3201938.0 | 108.0 | SEA | BUR | Burbank | CA |
| 3201655.0 | 107.0 | SEA | SNA | Orange County | CA |
| 1011950.0 | 123.0 | SEA | OAK | Oakland | CA |
| 1021950.0 | 194.0 | SEA | OAK | Oakland | CA |
| 1021615.0 | 317.0 | SEA | OAK | Oakland | CA |
| 1021755.0 | 385.0 | SEA | OAK | Oakland | CA |
| 1031950.0 | 283.0 | SEA | OAK | Oakland | CA |
| 1031615.0 | 364.0 | SEA | OAK | Oakland | CA |
| 1031325.0 | 130.0 | SEA | OAK | Oakland | CA |
| 1061755.0 | 107.0 | SEA | OAK | Oakland | CA |
| 1081330.0 | 118.0 | SEA | OAK | Oakland | CA |
| 2282055.0 | 150.0 | SEA | OAK | Oakland | CA |
| 3061600.0 | 130.0 | SEA | OAK | Oakland | CA |
| 3170815.0 | 199.0 | SEA | DCA | Washington DC | null |
| 2151845.0 | 128.0 | SEA | KTN | Ketchikan | AK |
| 2281845.0 | 104.0 | SEA | KTN | Ketchikan | AK |
| 3130720.0 | 117.0 | SEA | KTN | Ketchikan | AK |
| 1011411.0 | 177.0 | SEA | IAH | Houston | TX |
| 1022347.0 | 158.0 | SEA | IAH | Houston | TX |
| 1021411.0 | 170.0 | SEA | IAH | Houston | TX |
| 1031201.0 | 116.0 | SEA | IAH | Houston | TX |
| 1031900.0 | 178.0 | SEA | IAH | Houston | TX |
| 1042347.0 | 114.0 | SEA | IAH | Houston | TX |
| 1062347.0 | 136.0 | SEA | IAH | Houston | TX |
| 1101203.0 | 173.0 | SEA | IAH | Houston | TX |
| 1172300.0 | 125.0 | SEA | IAH | Houston | TX |
| 1250605.0 | 227.0 | SEA | IAH | Houston | TX |
| 1291203.0 | 106.0 | SEA | IAH | Houston | TX |
| 2141157.0 | 127.0 | SEA | IAH | Houston | TX |
| 2150740.0 | 466.0 | SEA | IAH | Houston | TX |
| 2180750.0 | 105.0 | SEA | IAH | Houston | TX |
| 3010740.0 | 103.0 | SEA | IAH | Houston | TX |
| 3021152.0 | 124.0 | SEA | IAH | Houston | TX |
| 3121152.0 | 340.0 | SEA | IAH | Houston | TX |
| 3140600.0 | 139.0 | SEA | IAH | Houston | TX |
| 3171152.0 | 121.0 | SEA | IAH | Houston | TX |
| 3280600.0 | 103.0 | SEA | IAH | Houston | TX |
| 1151746.0 | 229.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1271746.0 | 111.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1030840.0 | 294.0 | SEA | HNL | Honolulu, Oahu | HI |
| 2091035.0 | 132.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3141035.0 | 295.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3141930.0 | 128.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3311930.0 | 104.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3030840.0 | 185.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3240845.0 | 114.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1041740.0 | 256.0 | SEA | SJC | San Jose | CA |
| 1061815.0 | 200.0 | SEA | SJC | San Jose | CA |
| 3031600.0 | 161.0 | SEA | SJC | San Jose | CA |
| 1031100.0 | 145.0 | SEA | LGB | Long Beach | CA |
| 1041737.0 | 320.0 | SEA | LGB | Long Beach | CA |
| 1081728.0 | 122.0 | SEA | LGB | Long Beach | CA |
| 1221140.0 | 189.0 | SEA | LGB | Long Beach | CA |
| 2121140.0 | 365.0 | SEA | LGB | Long Beach | CA |
| 3070710.0 | 115.0 | SEA | LGB | Long Beach | CA |
| 3180935.0 | 135.0 | SEA | LGB | Long Beach | CA |
| 2141245.0 | 176.0 | SEA | RNO | Reno | NV |
| 1052147.0 | 110.0 | SEA | BOS | Boston | MA |
| 1081420.0 | 109.0 | SEA | BOS | Boston | MA |
| 1112313.0 | 110.0 | SEA | BOS | Boston | MA |
| 1232313.0 | 110.0 | SEA | BOS | Boston | MA |
| 2140945.0 | 105.0 | SEA | BOS | Boston | MA |
| 2032313.0 | 114.0 | SEA | BOS | Boston | MA |
| 2121420.0 | 108.0 | SEA | BOS | Boston | MA |
| 2141415.0 | 152.0 | SEA | BOS | Boston | MA |
| 3282307.0 | 119.0 | SEA | BOS | Boston | MA |
| 1110905.0 | 130.0 | SEA | EWR | Newark | NJ |
| 1022227.0 | 236.0 | SEA | EWR | Newark | NJ |
| 1180703.0 | 138.0 | SEA | EWR | Newark | NJ |
| 2030905.0 | 212.0 | SEA | EWR | Newark | NJ |
| 3141530.0 | 131.0 | SEA | EWR | Newark | NJ |
| 3171530.0 | 181.0 | SEA | EWR | Newark | NJ |
| 3202200.0 | 168.0 | SEA | EWR | Newark | NJ |
| 1032115.0 | 196.0 | SEA | LAS | Las Vegas | NV |
| 1201840.0 | 113.0 | SEA | LAS | Las Vegas | NV |
| 1260840.0 | 165.0 | SEA | LAS | Las Vegas | NV |
| 1261840.0 | 121.0 | SEA | LAS | Las Vegas | NV |
| 1031905.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 1041550.0 | 156.0 | SEA | LAS | Las Vegas | NV |
| 1061820.0 | 213.0 | SEA | LAS | Las Vegas | NV |
| 1061515.0 | 116.0 | SEA | LAS | Las Vegas | NV |
| 1080805.0 | 235.0 | SEA | LAS | Las Vegas | NV |
| 1170800.0 | 247.0 | SEA | LAS | Las Vegas | NV |
| 2191235.0 | 210.0 | SEA | LAS | Las Vegas | NV |
| 2281430.0 | 121.0 | SEA | LAS | Las Vegas | NV |
| 2281235.0 | 187.0 | SEA | LAS | Las Vegas | NV |
| 2170830.0 | 610.0 | SEA | LAS | Las Vegas | NV |
| 2051205.0 | 110.0 | SEA | LAS | Las Vegas | NV |
| 2111835.0 | 133.0 | SEA | LAS | Las Vegas | NV |
| 2132005.0 | 135.0 | SEA | LAS | Las Vegas | NV |
| 2272005.0 | 102.0 | SEA | LAS | Las Vegas | NV |
| 3031430.0 | 108.0 | SEA | LAS | Las Vegas | NV |
| 3141410.0 | 102.0 | SEA | LAS | Las Vegas | NV |
| 3151205.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 3281245.0 | 140.0 | SEA | LAS | Las Vegas | NV |
| 3181520.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 3292000.0 | 107.0 | SEA | LAS | Las Vegas | NV |
| 1051955.0 | 160.0 | SEA | FAI | Fairbanks | AK |
| 1040955.0 | 126.0 | SEA | DEN | Denver | CO |
| 1040650.0 | 103.0 | SEA | DEN | Denver | CO |
| 1160650.0 | 120.0 | SEA | DEN | Denver | CO |
| 1011940.0 | 167.0 | SEA | DEN | Denver | CO |
| 1041039.0 | 113.0 | SEA | DEN | Denver | CO |
| 1041437.0 | 256.0 | SEA | DEN | Denver | CO |
| 1041937.0 | 191.0 | SEA | DEN | Denver | CO |
| 1040605.0 | 138.0 | SEA | DEN | Denver | CO |
| 1061937.0 | 111.0 | SEA | DEN | Denver | CO |
| 1121937.0 | 118.0 | SEA | DEN | Denver | CO |
| 1171937.0 | 104.0 | SEA | DEN | Denver | CO |
| 1021523.0 | 118.0 | SEA | DEN | Denver | CO |
| 1041521.0 | 177.0 | SEA | DEN | Denver | CO |
| 1061055.0 | 154.0 | SEA | DEN | Denver | CO |
| 1281515.0 | 137.0 | SEA | DEN | Denver | CO |
| 1011450.0 | 102.0 | SEA | DEN | Denver | CO |
| 1021205.0 | 425.0 | SEA | DEN | Denver | CO |
| 1031450.0 | 211.0 | SEA | DEN | Denver | CO |
| 2210955.0 | 132.0 | SEA | DEN | Denver | CO |
| 2111537.0 | 156.0 | SEA | DEN | Denver | CO |
| 2110700.0 | 625.0 | SEA | DEN | Denver | CO |
| 2191039.0 | 148.0 | SEA | DEN | Denver | CO |
| 2211039.0 | 105.0 | SEA | DEN | Denver | CO |
| 2031055.0 | 174.0 | SEA | DEN | Denver | CO |
| 2051055.0 | 112.0 | SEA | DEN | Denver | CO |
| 2211110.0 | 106.0 | SEA | DEN | Denver | CO |
| 3131405.0 | 154.0 | SEA | DEN | Denver | CO |
| 3181930.0 | 115.0 | SEA | DEN | Denver | CO |
| 3191749.0 | 103.0 | SEA | DEN | Denver | CO |
| 3011405.0 | 109.0 | SEA | DEN | Denver | CO |
| 3121519.0 | 231.0 | SEA | DEN | Denver | CO |
| 3061445.0 | 148.0 | SEA | DEN | Denver | CO |
| 3181440.0 | 132.0 | SEA | DEN | Denver | CO |
| 1011306.0 | 206.0 | SEA | IAD | Washington DC | null |
| 1050806.0 | 105.0 | SEA | IAD | Washington DC | null |
| 1052226.0 | 111.0 | SEA | IAD | Washington DC | null |
| 1291256.0 | 108.0 | SEA | IAD | Washington DC | null |
| 2031256.0 | 185.0 | SEA | IAD | Washington DC | null |
| 2041256.0 | 129.0 | SEA | IAD | Washington DC | null |
| 2101256.0 | 144.0 | SEA | IAD | Washington DC | null |
| 2191316.0 | 122.0 | SEA | IAD | Washington DC | null |
| 3080800.0 | 273.0 | SEA | IAD | Washington DC | null |
| 3162225.0 | 174.0 | SEA | IAD | Washington DC | null |
| 3201316.0 | 111.0 | SEA | IAD | Washington DC | null |
| 3261311.0 | 104.0 | SEA | IAD | Washington DC | null |
| 1111300.0 | 133.0 | SEA | PSP | Palm Springs | CA |
| 1110910.0 | 171.0 | SEA | PSP | Palm Springs | CA |
| 3170915.0 | 114.0 | SEA | PSP | Palm Springs | CA |
| 3310645.0 | 179.0 | SEA | PSP | Palm Springs | CA |
| 1161050.0 | 132.0 | SEA | SBA | Santa Barbara | CA |
| 2091050.0 | 310.0 | SEA | SBA | Santa Barbara | CA |
| 3191045.0 | 109.0 | SEA | SBA | Santa Barbara | CA |
| 3241045.0 | 140.0 | SEA | SBA | Santa Barbara | CA |
| 2122225.0 | 228.0 | SEA | CLT | Charlotte | NC |
| 2142225.0 | 104.0 | SEA | CLT | Charlotte | NC |
| 2152225.0 | 113.0 | SEA | CLT | Charlotte | NC |
| 3081110.0 | 110.0 | SEA | CLT | Charlotte | NC |
| 1290940.0 | 316.0 | SEA | ABQ | Albuquerque | NM |
| 1171153.0 | 119.0 | SEA | PDX | Portland | OR |
| 1171439.0 | 112.0 | SEA | PDX | Portland | OR |
| 2091041.0 | 309.0 | SEA | PDX | Portland | OR |
| 2090845.0 | 133.0 | SEA | PDX | Portland | OR |
| 3021455.0 | 226.0 | SEA | PDX | Portland | OR |
| 3021914.0 | 165.0 | SEA | PDX | Portland | OR |
| 3090926.0 | 112.0 | SEA | PDX | Portland | OR |
| 3251041.0 | 274.0 | SEA | PDX | Portland | OR |
| 3261728.0 | 109.0 | SEA | PDX | Portland | OR |
| 3301728.0 | 108.0 | SEA | PDX | Portland | OR |
| 1112205.0 | 101.0 | SEA | MIA | Miami | FL |
| 1262205.0 | 130.0 | SEA | MIA | Miami | FL |
| 2142215.0 | 229.0 | SEA | MIA | Miami | FL |
| 3292220.0 | 117.0 | SEA | MIA | Miami | FL |
| 1012140.0 | 136.0 | SEA | SMF | Sacramento | CA |
| 1010715.0 | 164.0 | SEA | SMF | Sacramento | CA |
| 1021930.0 | 109.0 | SEA | SMF | Sacramento | CA |
| 1031630.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 1041440.0 | 137.0 | SEA | SMF | Sacramento | CA |
| 1041710.0 | 102.0 | SEA | SMF | Sacramento | CA |
| 2211915.0 | 103.0 | SEA | SMF | Sacramento | CA |
| 3262140.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 3271855.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 3191020.0 | 139.0 | SEA | SMF | Sacramento | CA |
| 1291410.0 | 136.0 | SEA | PHX | Phoenix | AZ |
| 1040830.0 | 294.0 | SEA | PHX | Phoenix | AZ |
| 1300830.0 | 371.0 | SEA | PHX | Phoenix | AZ |
| 1031510.0 | 119.0 | SEA | PHX | Phoenix | AZ |
| 1061510.0 | 180.0 | SEA | PHX | Phoenix | AZ |
| 1290720.0 | 147.0 | SEA | PHX | Phoenix | AZ |
| 2082020.0 | 130.0 | SEA | PHX | Phoenix | AZ |
| 2281855.0 | 186.0 | SEA | PHX | Phoenix | AZ |
| 2110520.0 | 463.0 | SEA | PHX | Phoenix | AZ |
| 2111132.0 | 107.0 | SEA | PHX | Phoenix | AZ |
| 2160830.0 | 110.0 | SEA | PHX | Phoenix | AZ |
| 2241132.0 | 107.0 | SEA | PHX | Phoenix | AZ |
| 2251615.0 | 103.0 | SEA | PHX | Phoenix | AZ |
| 3041455.0 | 108.0 | SEA | PHX | Phoenix | AZ |
| 3051745.0 | 169.0 | SEA | PHX | Phoenix | AZ |
| 3091455.0 | 126.0 | SEA | PHX | Phoenix | AZ |
| 3251132.0 | 123.0 | SEA | PHX | Phoenix | AZ |
| 3221530.0 | 121.0 | SEA | PHX | Phoenix | AZ |
| 1060830.0 | 116.0 | SEA | DFW | Dallas | TX |
| 1180830.0 | 132.0 | SEA | DFW | Dallas | TX |
| 1281350.0 | 115.0 | SEA | DFW | Dallas | TX |
| 1291545.0 | 247.0 | SEA | DFW | Dallas | TX |
| 2031545.0 | 230.0 | SEA | DFW | Dallas | TX |
| 2040830.0 | 135.0 | SEA | DFW | Dallas | TX |
| 2061115.0 | 110.0 | SEA | DFW | Dallas | TX |
| 2061545.0 | 125.0 | SEA | DFW | Dallas | TX |
| 2061350.0 | 126.0 | SEA | DFW | Dallas | TX |
| 2080830.0 | 356.0 | SEA | DFW | Dallas | TX |
| 2090830.0 | 128.0 | SEA | DFW | Dallas | TX |
| 3012320.0 | 123.0 | SEA | DFW | Dallas | TX |
| 3152320.0 | 143.0 | SEA | DFW | Dallas | TX |
| 3151400.0 | 146.0 | SEA | DFW | Dallas | TX |
| 3301400.0 | 277.0 | SEA | DFW | Dallas | TX |
| 1171220.0 | 110.0 | SEA | SFO | San Francisco | CA |
| 1291220.0 | 103.0 | SEA | SFO | San Francisco | CA |
| 1031125.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 1120924.0 | 268.0 | SEA | SFO | San Francisco | CA |
| 1161058.0 | 131.0 | SEA | SFO | San Francisco | CA |
| 1171058.0 | 138.0 | SEA | SFO | San Francisco | CA |
| 1010955.0 | 104.0 | SEA | SFO | San Francisco | CA |
| 1021914.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 1041830.0 | 131.0 | SEA | SFO | San Francisco | CA |
| 1051214.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 1061214.0 | 384.0 | SEA | SFO | San Francisco | CA |
| 1111310.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 1241310.0 | 133.0 | SEA | SFO | San Francisco | CA |
| 1281310.0 | 250.0 | SEA | SFO | San Francisco | CA |
| 1051855.0 | 166.0 | SEA | SFO | San Francisco | CA |
| 2060955.0 | 148.0 | SEA | SFO | San Francisco | CA |
| 2072125.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2071845.0 | 203.0 | SEA | SFO | San Francisco | CA |
| 2071220.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2071100.0 | 113.0 | SEA | SFO | San Francisco | CA |
| 2071405.0 | 144.0 | SEA | SFO | San Francisco | CA |
| 2080955.0 | 121.0 | SEA | SFO | San Francisco | CA |
| 2091835.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 2091405.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 2101220.0 | 134.0 | SEA | SFO | San Francisco | CA |
| 2100955.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 2101100.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2101405.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2130955.0 | 108.0 | SEA | SFO | San Francisco | CA |
| 2131100.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2141100.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 2281100.0 | 130.0 | SEA | SFO | San Francisco | CA |
| 2231058.0 | 136.0 | SEA | SFO | San Francisco | CA |
| 2021310.0 | 115.0 | SEA | SFO | San Francisco | CA |
| 2091820.0 | 149.0 | SEA | SFO | San Francisco | CA |
| 2181526.0 | 106.0 | SEA | SFO | San Francisco | CA |
| 2271905.0 | 154.0 | SEA | SFO | San Francisco | CA |
| 2021450.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2060950.0 | 150.0 | SEA | SFO | San Francisco | CA |
| 2061450.0 | 140.0 | SEA | SFO | San Francisco | CA |
| 2061855.0 | 119.0 | SEA | SFO | San Francisco | CA |
| 2071855.0 | 146.0 | SEA | SFO | San Francisco | CA |
| 2081330.0 | 164.0 | SEA | SFO | San Francisco | CA |
| 2091450.0 | 106.0 | SEA | SFO | San Francisco | CA |
| 2091855.0 | 182.0 | SEA | SFO | San Francisco | CA |
| 2261450.0 | 259.0 | SEA | SFO | San Francisco | CA |
| 2261855.0 | 224.0 | SEA | SFO | San Francisco | CA |
| 2270950.0 | 174.0 | SEA | SFO | San Francisco | CA |
| 2271450.0 | 240.0 | SEA | SFO | San Francisco | CA |
| 2280950.0 | 207.0 | SEA | SFO | San Francisco | CA |
| 2281450.0 | 331.0 | SEA | SFO | San Francisco | CA |
| 2281855.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 3142043.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 3180950.0 | 155.0 | SEA | SFO | San Francisco | CA |
| 3260950.0 | 117.0 | SEA | SFO | San Francisco | CA |
| 3281430.0 | 112.0 | SEA | SFO | San Francisco | CA |
| 3291050.0 | 138.0 | SEA | SFO | San Francisco | CA |
| 3062055.0 | 113.0 | SEA | SFO | San Francisco | CA |
| 3091028.0 | 143.0 | SEA | SFO | San Francisco | CA |
| 3112055.0 | 116.0 | SEA | SFO | San Francisco | CA |
| 3261043.0 | 121.0 | SEA | SFO | San Francisco | CA |
| 3311043.0 | 257.0 | SEA | SFO | San Francisco | CA |
| 3171237.0 | 182.0 | SEA | SFO | San Francisco | CA |
| 3251933.0 | 197.0 | SEA | SFO | San Francisco | CA |
| 3011330.0 | 115.0 | SEA | SFO | San Francisco | CA |
| 3311045.0 | 189.0 | SEA | SFO | San Francisco | CA |
| 3311440.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 1031152.0 | 397.0 | SEA | ATL | Atlanta | GA |
| 1050830.0 | 106.0 | SEA | ATL | Atlanta | GA |
| 1051152.0 | 107.0 | SEA | ATL | Atlanta | GA |
| 1121159.0 | 201.0 | SEA | ATL | Atlanta | GA |
| 1250630.0 | 206.0 | SEA | ATL | Atlanta | GA |
| 1301159.0 | 117.0 | SEA | ATL | Atlanta | GA |
| 1301322.0 | 179.0 | SEA | ATL | Atlanta | GA |
| 2021159.0 | 145.0 | SEA | ATL | Atlanta | GA |
| 2171320.0 | 133.0 | SEA | ATL | Atlanta | GA |
| 3171320.0 | 109.0 | SEA | ATL | Atlanta | GA |
| 1092015.0 | 119.0 | SEA | FAT | Fresno | CA |
| 2012015.0 | 189.0 | SEA | FAT | Fresno | CA |
| 2091150.0 | 232.0 | SEA | FAT | Fresno | CA |
| 1021425.0 | 298.0 | SEA | ORD | Chicago | IL |
| 1030600.0 | 103.0 | SEA | ORD | Chicago | IL |
| 1081205.0 | 135.0 | SEA | ORD | Chicago | IL |
| 1161205.0 | 582.0 | SEA | ORD | Chicago | IL |
| 1241205.0 | 174.0 | SEA | ORD | Chicago | IL |
| 1300815.0 | 209.0 | SEA | ORD | Chicago | IL |
| 1021235.0 | 113.0 | SEA | ORD | Chicago | IL |
| 1020830.0 | 151.0 | SEA | ORD | Chicago | IL |
| 1051235.0 | 240.0 | SEA | ORD | Chicago | IL |
| 1050830.0 | 163.0 | SEA | ORD | Chicago | IL |
| 1241235.0 | 171.0 | SEA | ORD | Chicago | IL |
| 1301235.0 | 111.0 | SEA | ORD | Chicago | IL |
| 1300830.0 | 141.0 | SEA | ORD | Chicago | IL |
| 1012359.0 | 227.0 | SEA | ORD | Chicago | IL |
| 1040859.0 | 302.0 | SEA | ORD | Chicago | IL |
| 1241110.0 | 223.0 | SEA | ORD | Chicago | IL |
| 1311411.0 | 142.0 | SEA | ORD | Chicago | IL |
| 2051235.0 | 115.0 | SEA | ORD | Chicago | IL |
| 2050830.0 | 128.0 | SEA | ORD | Chicago | IL |
| 2171235.0 | 204.0 | SEA | ORD | Chicago | IL |
| 2170830.0 | 220.0 | SEA | ORD | Chicago | IL |
| 2031615.0 | 185.0 | SEA | ORD | Chicago | IL |
| 2171415.0 | 164.0 | SEA | ORD | Chicago | IL |
| 2261415.0 | 138.0 | SEA | ORD | Chicago | IL |
| 3120820.0 | 179.0 | SEA | ORD | Chicago | IL |
| 3121200.0 | 151.0 | SEA | ORD | Chicago | IL |
| 3200600.0 | 140.0 | SEA | ORD | Chicago | IL |
| 3051235.0 | 140.0 | SEA | ORD | Chicago | IL |
| 3120830.0 | 204.0 | SEA | ORD | Chicago | IL |
| 3132240.0 | 127.0 | SEA | ORD | Chicago | IL |
| 3162240.0 | 150.0 | SEA | ORD | Chicago | IL |
| 3181417.0 | 127.0 | SEA | ORD | Chicago | IL |
| 1091350.0 | 133.0 | SEA | MDW | Chicago | IL |
| 1261350.0 | 109.0 | SEA | MDW | Chicago | IL |
| 3171420.0 | 111.0 | SEA | MDW | Chicago | IL |
| 3310600.0 | 206.0 | SEA | MDW | Chicago | IL |
| 2271820.0 | 203.0 | SEA | COS | Colorado Springs | CO |
| 3171825.0 | 205.0 | SEA | COS | Colorado Springs | CO |
| 1250755.0 | 233.0 | SEA | JNU | Juneau | AK |
| 1310755.0 | 210.0 | SEA | JNU | Juneau | AK |
| 1311120.0 | 105.0 | SEA | JNU | Juneau | AK |
| 3030755.0 | 110.0 | SEA | JNU | Juneau | AK |
| 3130750.0 | 320.0 | SEA | JNU | Juneau | AK |
| 1062320.0 | 107.0 | SEA | DTW | Detroit | MI |
| 3121405.0 | 135.0 | SEA | ONT | Ontario | CA |
| 3130725.0 | 174.0 | SEA | ONT | Ontario | CA |
| 1171425.0 | 105.0 | SEA | LAX | Los Angeles | CA |
| 1041700.0 | 264.0 | SEA | LAX | Los Angeles | CA |
| 1051700.0 | 136.0 | SEA | LAX | Los Angeles | CA |
| 1071645.0 | 151.0 | SEA | LAX | Los Angeles | CA |
| 1311645.0 | 136.0 | SEA | LAX | Los Angeles | CA |
| 1251020.0 | 130.0 | SEA | LAX | Los Angeles | CA |
| 1261020.0 | 108.0 | SEA | LAX | Los Angeles | CA |
| 1291258.0 | 126.0 | SEA | LAX | Los Angeles | CA |
| 2022140.0 | 131.0 | SEA | LAX | Los Angeles | CA |
| 2130610.0 | 135.0 | SEA | LAX | Los Angeles | CA |
| 2091645.0 | 104.0 | SEA | LAX | Los Angeles | CA |
| 2131645.0 | 134.0 | SEA | LAX | Los Angeles | CA |
| 2031805.0 | 212.0 | SEA | LAX | Los Angeles | CA |
| 2071805.0 | 105.0 | SEA | LAX | Los Angeles | CA |
| 2071649.0 | 126.0 | SEA | LAX | Los Angeles | CA |
| 2081649.0 | 154.0 | SEA | LAX | Los Angeles | CA |
| 2091649.0 | 245.0 | SEA | LAX | Los Angeles | CA |
| 2131705.0 | 107.0 | SEA | LAX | Los Angeles | CA |
| 2161010.0 | 124.0 | SEA | LAX | Los Angeles | CA |
| 2271149.0 | 192.0 | SEA | LAX | Los Angeles | CA |
| 3141650.0 | 197.0 | SEA | LAX | Los Angeles | CA |
| 3152105.0 | 115.0 | SEA | LAX | Los Angeles | CA |
| 3171700.0 | 131.0 | SEA | LAX | Los Angeles | CA |
| 3291700.0 | 118.0 | SEA | LAX | Los Angeles | CA |
| 1050037.0 | 102.0 | SEA | MSP | Minneapolis | MN |
| 1070700.0 | 193.0 | SEA | MSP | Minneapolis | MN |
| 1130700.0 | 429.0 | SEA | MSP | Minneapolis | MN |
| 2240655.0 | 109.0 | SEA | MSP | Minneapolis | MN |
| 2121515.0 | 118.0 | SEA | MSP | Minneapolis | MN |
| 1060800.0 | 132.0 | SEA | MCO | Orlando | FL |
| 2220955.0 | 157.0 | SEA | SAN | San Diego | CA |
| 3010955.0 | 108.0 | SEA | SAN | San Diego | CA |
| 1031300.0 | 108.0 | SEA | ANC | Anchorage | AK |
| 1062110.0 | 149.0 | SEA | ANC | Anchorage | AK |
| 1132112.0 | 106.0 | SEA | ANC | Anchorage | AK |
| 2072350.0 | 141.0 | SEA | ANC | Anchorage | AK |
| 2091815.0 | 133.0 | SEA | ANC | Anchorage | AK |
| 3181605.0 | 187.0 | SEA | ANC | Anchorage | AK |
| 3231915.0 | 115.0 | SEA | ANC | Anchorage | AK |
| 3311605.0 | 135.0 | SEA | ANC | Anchorage | AK |
| 1022258.0 | 180.0 | SEA | JFK | New York | NY |
| 1042258.0 | 285.0 | SEA | JFK | New York | NY |
| 1022259.0 | 116.0 | SEA | JFK | New York | NY |
| 2090715.0 | 110.0 | SEA | JFK | New York | NY |
| 2022145.0 | 101.0 | SEA | JFK | New York | NY |
| 2032145.0 | 165.0 | SEA | JFK | New York | NY |
| 2031535.0 | 181.0 | SEA | JFK | New York | NY |
| 2042145.0 | 201.0 | SEA | JFK | New York | NY |
| 2142140.0 | 113.0 | SEA | JFK | New York | NY |
| 2222140.0 | 118.0 | SEA | JFK | New York | NY |
| 2032258.0 | 130.0 | SEA | JFK | New York | NY |
| 2220700.0 | 404.0 | SEA | JFK | New York | NY |
| 3310715.0 | 385.0 | SEA | JFK | New York | NY |
| 3192140.0 | 119.0 | SEA | JFK | New York | NY |
| 3091300.0 | 125.0 | SEA | JFK | New York | NY |
| 1241000.0 | 806.0 | SEA | OGG | Kahului, Maui | HI |
| 1030845.0 | 342.0 | SEA | PHL | Philadelphia | PA |
| 1050845.0 | 174.0 | SEA | PHL | Philadelphia | PA |
| 1210845.0 | 866.0 | SEA | PHL | Philadelphia | PA |
| 1022215.0 | 121.0 | SEA | PHL | Philadelphia | PA |
| 1031130.0 | 146.0 | SEA | PHL | Philadelphia | PA |
| 1090835.0 | 148.0 | SEA | PHL | Philadelphia | PA |
| 1170835.0 | 203.0 | SEA | PHL | Philadelphia | PA |
| 2030845.0 | 202.0 | SEA | PHL | Philadelphia | PA |
| 3130835.0 | 290.0 | SEA | PHL | Philadelphia | PA |
| 1031810.0 | 142.0 | SEA | SLC | Salt Lake City | UT |
| 1041016.0 | 117.0 | SEA | SLC | Salt Lake City | UT |
| 1291725.0 | 111.0 | SEA | SLC | Salt Lake City | UT |
| 1021555.0 | 175.0 | SEA | SLC | Salt Lake City | UT |
| 1041825.0 | 110.0 | SEA | SLC | Salt Lake City | UT |
| 2131635.0 | 134.0 | SEA | SLC | Salt Lake City | UT |
| 2230710.0 | 739.0 | SEA | SLC | Salt Lake City | UT |
| 2240710.0 | 477.0 | SEA | SLC | Salt Lake City | UT |
| 2061005.0 | 119.0 | SEA | SLC | Salt Lake City | UT |
| 3051315.0 | 123.0 | SEA | SLC | Salt Lake City | UT |
| 3260710.0 | 149.0 | SEA | SLC | Salt Lake City | UT |
| 3281750.0 | 125.0 | SEA | SLC | Salt Lake City | UT |
Vertex Degrees
inDegrees: Incoming connections to the airportoutDegrees: Outgoing connections from the airportdegrees: Total connections to and from the airport
Reviewing the various properties of the property graph to understand the incoming and outgoing connections between airports.
// Degrees
// The number of degrees - the number of incoming and outgoing connections - for various airports within this sample dataset
display(tripGraph.degrees.sort($"degree".desc).limit(20))
| id | degree |
|---|---|
| ATL | 179774.0 |
| DFW | 133966.0 |
| ORD | 125405.0 |
| LAX | 106853.0 |
| DEN | 103699.0 |
| IAH | 85685.0 |
| PHX | 79672.0 |
| SFO | 77635.0 |
| LAS | 66101.0 |
| CLT | 56103.0 |
| EWR | 54407.0 |
| MCO | 54300.0 |
| LGA | 50927.0 |
| SLC | 50780.0 |
| BOS | 49936.0 |
| DTW | 46705.0 |
| MSP | 46235.0 |
| SEA | 45816.0 |
| JFK | 43661.0 |
| BWI | 42526.0 |
City / Flight Relationships through Motif Finding
To more easily understand the complex relationship of city airports and their flights with each other, we can use motifs to find patterns of airports (i.e. vertices) connected by flights (i.e. edges). The result is a DataFrame in which the column names are given by the motif keys.
/*
Using tripGraphPrime to more easily display
- The associated edge (ab, bc) relationships
- With the different the city / airports (a, b, c) where SFO is the connecting city (b)
- Ensuring that flight ab (i.e. the flight to SFO) occured before flight bc (i.e. flight leaving SFO)
- Note, TripID was generated based on time in the format of MMDDHHMM converted to int
- Therefore bc.tripid < ab.tripid + 10000 means the second flight (bc) occured within approx a day of the first flight (ab)
Note: In reality, we would need to be more careful to link trips ab and bc.
*/
val motifs = tripGraphPrime.
find("(a)-[ab]->(b); (b)-[bc]->(c)").
filter("(b.id = 'SFO') and (ab.delay > 500 or bc.delay > 500) and bc.tripid > ab.tripid and bc.tripid < ab.tripid + 10000")
display(motifs)
Determining Airport Ranking using PageRank
There are a large number of flights and connections through these various airports included in this Departure Delay Dataset. Using the pageRank algorithm, Spark iteratively traverses the graph and determines a rough estimate of how important the airport is.
// Determining Airport ranking of importance using `pageRank`
val ranks = tripGraph.pageRank.resetProbability(0.15).maxIter(5).run()
ranks: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 3 more fields], e:[src: string, dst: string ... 5 more fields])
display(ranks.vertices.orderBy($"pagerank".desc).limit(20))
| id | City | State | Country | pagerank |
|---|---|---|---|---|
| ATL | Atlanta | GA | USA | 18.910104616729814 |
| DFW | Dallas | TX | USA | 13.699227467378964 |
| ORD | Chicago | IL | USA | 13.163049993795985 |
| DEN | Denver | CO | USA | 9.723388283811563 |
| LAX | Los Angeles | CA | USA | 8.703656827807166 |
| IAH | Houston | TX | USA | 7.991324463091128 |
| SFO | San Francisco | CA | USA | 6.903242998287933 |
| PHX | Phoenix | AZ | USA | 6.505886984498643 |
| SLC | Salt Lake City | UT | USA | 5.799587684561128 |
| LAS | Las Vegas | NV | USA | 5.25359244560915 |
| SEA | Seattle | WA | USA | 4.626877547905697 |
| EWR | Newark | NJ | USA | 4.401221169028188 |
| MCO | Orlando | FL | USA | 4.389045874474043 |
| CLT | Charlotte | NC | USA | 4.378459524081744 |
| DTW | Detroit | MI | USA | 4.223377976049847 |
| MSP | Minneapolis | MN | USA | 4.1490048912541795 |
| LGA | New York | NY | USA | 4.129454491295321 |
| BOS | Boston | MA | USA | 3.812077076528526 |
| BWI | Baltimore | MD | USA | 3.53116352570383 |
| JFK | New York | NY | USA | 3.521942669296845 |
BTW, A lot more delicate air-traffic arithmetic is possible for a full month of airplane co-trajectories over the radar range of Atlanta, Georgia, one of the busiest airports in the world.
See for instance:
- Statistical regular pavings to analyze massive data of aircraft trajectories, Gloria Teng, Kenneth Kuhn and Raazesh Sainudiin, Journal of Aerospace Computing, Information, and Communication, Vol. 9, No. 1, pp. 14-25, doi: 10.2514/1.I010015, 2012. See free preprint: http://lamastex.org/preprints/AAIASubPavingATC.pdf.
Most popular flights (single city hops)
Using the tripGraph, we can quickly determine what are the most popular single city hop flights
// Determine the most popular flights (single city hops)
import org.apache.spark.sql.functions._
val topTrips = tripGraph.edges.
groupBy("src", "dst").
agg(count("delay").as("trips"))
import org.apache.spark.sql.functions._
topTrips: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
// Show the top 20 most popular flights (single city hops)
display(topTrips.orderBy($"trips".desc).limit(20))
| src | dst | trips |
|---|---|---|
| SFO | LAX | 3232.0 |
| LAX | SFO | 3198.0 |
| LAS | LAX | 3016.0 |
| LAX | LAS | 2964.0 |
| JFK | LAX | 2720.0 |
| LAX | JFK | 2719.0 |
| ATL | LGA | 2501.0 |
| LGA | ATL | 2500.0 |
| LAX | PHX | 2394.0 |
| PHX | LAX | 2387.0 |
| HNL | OGG | 2380.0 |
| OGG | HNL | 2379.0 |
| LAX | SAN | 2215.0 |
| SAN | LAX | 2214.0 |
| SJC | LAX | 2208.0 |
| LAX | SJC | 2201.0 |
| ATL | MCO | 2136.0 |
| MCO | ATL | 2090.0 |
| JFK | SFO | 2084.0 |
| SFO | JFK | 2084.0 |
Top Transfer Cities
Many airports are used as transfer points instead of the final Destination. An easy way to calculate this is by calculating the ratio of inDegree (the number of flights to the airport) / outDegree (the number of flights leaving the airport). Values close to 1 may indicate many transfers, whereas values < 1 indicate many outgoing flights and > 1 indicate many incoming flights. Note, this is a simple calculation that does not take into account of timing or scheduling of flights, just the overall aggregate number within the dataset.
// Calculate the inDeg (flights into the airport) and outDeg (flights leaving the airport)
val inDeg = tripGraph.inDegrees
val outDeg = tripGraph.outDegrees
// Calculate the degreeRatio (inDeg/outDeg), perform inner join on "id" column
val degreeRatio = inDeg.join(outDeg, inDeg("id") === outDeg("id")).
drop(outDeg("id")).
selectExpr("id", "double(inDegree)/double(outDegree) as degreeRatio").
cache()
// Join back to the `airports` DataFrame (instead of registering temp table as above)
val nonTransferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA").
selectExpr("id", "city", "degreeRatio").
filter("degreeRatio < 0.9 or degreeRatio > 1.1")
// List out the city airports which have abnormal degree ratios
display(nonTransferAirports)
| id | city | degreeRatio |
|---|---|---|
| GFK | Grand Forks | 1.3333333333333333 |
| FAI | Fairbanks | 1.1232686980609419 |
| OME | Nome | 0.5084745762711864 |
| BRW | Barrow | 0.28651685393258425 |
// Join back to the `airports` DataFrame (instead of registering temp table as above)
val transferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA"). //degreeRatio.join(airports, degreeRatio("id") === airports("IATA")).
selectExpr("id", "city", "degreeRatio").
filter("degreeRatio between 0.9 and 1.1")
// List out the top 10 transfer city airports
display(transferAirports.orderBy("degreeRatio").limit(10))
| id | city | degreeRatio |
|---|---|---|
| MSP | Minneapolis | 0.9375183338222353 |
| DEN | Denver | 0.958025717037065 |
| DFW | Dallas | 0.964339653074092 |
| ORD | Chicago | 0.9671063983310065 |
| SLC | Salt Lake City | 0.9827417906368358 |
| IAH | Houston | 0.9846895050147083 |
| PHX | Phoenix | 0.9891643572266746 |
| OGG | Kahului, Maui | 0.9898718478710211 |
| HNL | Honolulu, Oahu | 0.990535889872173 |
| SFO | San Francisco | 0.9909473252295224 |
Breadth First Search
Breadth-first search (BFS) is designed to traverse the graph to quickly find the desired vertices (i.e. airports) and edges (i.e flights). Let's try to find the shortest number of connections between cities based on the dataset. Note, these examples do not take into account of time or distance, just hops between cities.
// Example 1: Direct Seattle to San Francisco
// This method returns a DataFrame of valid shortest paths from vertices matching "fromExpr" to vertices matching "toExpr"
val filteredPaths = tripGraph.bfs.fromExpr((col("id") === "SEA")).toExpr(col("id") === "SFO").maxPathLength(1).run()
display(filteredPaths)
As you can see, there are a number of direct flights between Seattle and San Francisco.
// Example 2: Direct San Francisco and Buffalo
// You can also specify expression as a String, instead of Column
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(1).run()
filteredPaths: org.apache.spark.sql.DataFrame = [id: string, City: string ... 2 more fields]
filteredPaths.show()
+---+----+-----+-------+
| id|City|State|Country|
+---+----+-----+-------+
+---+----+-----+-------+
display(filteredPaths) // display instead of show - same diference
But there are no direct flights between San Francisco and Buffalo.
// Example 2a: Flying from San Francisco to Buffalo
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(2).run()
display(filteredPaths)
But there are flights from San Francisco to Buffalo with Minneapolis as the transfer point.
Loading the D3 Visualization
Using the airports D3 visualization to visualize airports and flight paths
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3a1.graphs.help()
Produces a force-directed graph given a collection of edges of the following form: case class Edge(src: String, dest: String, count: Long)
Usage:
%scala
import d3._
graphs.force(
height = 500,
width = 500,
clicks: Dataset[Edge])
// On-time and Early Arrivals
import d3a1._
graphs.force(
height = 800,
width = 1200,
clicks = sql("select src, dst as dest, count(1) as count from departureDelays_geo where delay <= 0 group by src, dst").as[Edge])

